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Darmowy szablon automatyzacji

Rozpoznaj wzorce dyskryminacji w miejscu pracy dzięki sztucznej inteligencji

3925
25 dni temu
38
bloków

Jak to działa

Ten szablon automatyzacji w n8n umożliwia kompleksową analizę opinii o firmach z serwisu Glassdoor pod kątem potencjalnych nierówności w miejscu pracy.

  • Pobiera dane opinii o firmach z Glassdoor przy użyciu ScrapingBee
  • Wykorzystuje analizę tekstu opartą na AI do wyodrębnienia ocen demograficznych
  • Oblicza nierówności w miejscu pracy przy użyciu miar statystycznych takich jak z-score, wielkość efektu i wartości p
  • Generuje wizualizacje (wykresy punktowe, słupkowe) uwidaczniające wzorce dyskryminacji lub uprzedzeń

Przykładowe wizualizacje

Szablon generuje czytelne wykresy prezentujące wyniki analizy nierówności w miejscu pracy.

Konfiguracja

Szacowany czas konfiguracji: ~20 minut

  • Zastąp dane dostępowe ScrapingBee i OpenAI własnymi
  • Wprowadź nazwę firmy do analizy (najlepsze wyniki dla dużych organizacji z siedzibą w USA)
  • Uruchom workflow i przejrzyj wygenerowane przez AI wnioski oraz raporty wizualne

Przykłady zastosowań

Ta automatyzacja znajduje zastosowanie w różnych scenariuszach związanych z analizą równości w miejscu pracy:

  • Monitorowanie różnic w ocenach pracowników w podziale na płeć, wiek czy pochodzenie etniczne
  • Identyfikacja potencjalnych obszarów dyskryminacji w dużych korporacjach
  • Przygotowanie raportów ESG dotyczących różnorodności i integracji
  • Wsparcie audytów wewnętrznych pod kątem równości szans
  • Badanie satysfakcji pracowników w różnych grupach demograficznych
  • Weryfikacja skuteczności programów różnorodnościowych
  • Dostarczanie danych do kampanii na rzecz sprawiedliwości społecznej

Korzyści

Workflow umożliwia użytkownikom identyfikację trendów potencjalnej dyskryminacji w miejscu pracy, wspierając działania na rzecz większej równości i odpowiedzialności.

Podziękowania dla: Wes Medford za algorytmy i inspirację.

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}}"},{"id":"0cf11453-dbae-4250-a01a-c98e35aab224","name":"population_analysis.z_scores.male","type":"number","value":"={{ ($json.output.male_average_rating - $json.average_rating) / ($json.std_dev / Math.sqrt($json.output.male_total_number_of_reviews)) }}"},{"id":"35a18fbc-7c2c-40fe-829d-2fffbdb13bb8","name":"population_analysis.z_scores.female","type":"number","value":"={{ ($json.output.female_average_rating - $json.average_rating) / ($json.std_dev / Math.sqrt($json.output.female_total_number_of_reviews)) }}"},{"id":"a6e17c1b-a89b-4c05-8184-10f7248c159f","name":"population_analysis.z_scores.trans","type":"number","value":"={{ ($json.output.trans_average_rating - $json.average_rating) / ($json.std_dev / Math.sqrt($json.output.trans_total_number_of_reviews)) }}"},{"id":"5e7dbccf-3011-4dba-863c-5390c1ee9e50","name":"population_analysis.z_scores.hetero","type":"number","value":"={{ ($json.output.hetero_average_rating - $json.average_rating) / ($json.std_dev / Math.sqrt($json.output.hetero_total_number_of_reviews)) }}"},{"id":"1872152f-2c7e-4c24-bcd5-e2777616bfe2","name":"population_analysis.z_scores.lgbtqia","type":"number","value":"={{ ($json.output.lgbtqia_average_rating - $json.average_rating) / ($json.std_dev / Math.sqrt($json.output.lgbtqia_total_number_of_reviews)) }}"},{"id":"91b2cb00-173e-421a-929a-51d2a6654767","name":"population_analysis.z_scores.nondisabled","type":"number","value":"={{ ($json.output.nondisabled_average_rating - $json.average_rating) / ($json.std_dev / Math.sqrt($json.output.nondisabled_total_number_of_reviews)) }}"},{"id":"8bb7429e-0500-482c-8e8d-d2c63733ffe1","name":"population_analysis.z_scores.disabled","type":"number","value":"={{ ($json.output.disabled_average_rating - $json.average_rating) / ($json.std_dev / Math.sqrt($json.output.disabled_total_number_of_reviews)) }}"},{"id":"89f00d0f-80db-4ad9-bf60-9385aa3d915b","name":"population_analysis.z_scores.caregiver","type":"number","value":"={{ ($json.output.caregiver_average_rating - $json.average_rating) / ($json.std_dev / Math.sqrt($json.output.caregiver_total_number_of_reviews)) }}"},{"id":"0bb2b96c-d882-4ac1-9432-9fce06b26cf5","name":"population_analysis.z_scores.parent","type":"number","value":"={{ ($json.output.parent_average_rating - $json.average_rating) / ($json.std_dev / Math.sqrt($json.output.parent_total_number_of_reviews)) }}"},{"id":"9aae7169-1a25-4fab-b940-7f2cd7ef39d9","name":"population_analysis.z_scores.nonparent","type":"number","value":"={{ ($json.output.nonparent_average_rating - $json.average_rating) / ($json.std_dev / Math.sqrt($json.output.nonparent_total_number_of_reviews)) }}"},{"id":"aac189a0-d6fc-4581-a15d-3e75a0cb370a","name":"population_analysis.z_scores.nonveteran","type":"number","value":"={{ ($json.output.nonveteran_average_rating - $json.average_rating) / ($json.std_dev / Math.sqrt($json.output.nonveteran_total_number_of_reviews)) }}"},{"id":"d40f014a-9c1d-4aea-88ac-d8a3de143931","name":"population_analysis.z_scores.veteran","type":"number","value":"={{ ($json.output.veteran_average_rating - $json.average_rating) / ($json.std_dev / Math.sqrt($json.output.veteran_total_number_of_reviews)) }}"},{"id":"67e0394f-6d55-4e80-8a7d-814635620b1d","name":"population_analysis.effect_sizes.hispanic","type":"number","value":"={{ ($json.output.hispanic_average_rating - $json.average_rating) / $json.std_dev }}"},{"id":"65cd3a22-2c97-4da1-8fcc-cc1af39118f2","name":"population_analysis.effect_sizes.white","type":"number","value":"={{ ($json.output.white_average_rating - $json.average_rating) / $json.std_dev }}"},{"id":"a03bdf0f-e294-4a01-bb08-ddc16e9997a5","name":"population_analysis.effect_sizes.pacific_islander","type":"number","value":"={{ ($json.output.pacific_islander_average_rating - $json.average_rating) / $json.std_dev }}"},{"id":"b0bdc40e-ed5f-475b-9d8b-8cf5beff7002","name":"population_analysis.effect_sizes.black","type":"number","value":"={{ ($json.output.black_average_rating - $json.average_rating) / $json.std_dev }}"},{"id":"45cac3f0-7270-4fa4-8fc4-94914245a77d","name":"population_analysis.effect_sizes.middle_eastern","type":"number","value":"={{ ($json.output.middle_eastern_average_rating - $json.average_rating) / $json.std_dev }}"},{"id":"cf5b7650-8766-45f6-8241-49aea62bf619","name":"population_analysis.effect_sizes.indigenous","type":"number","value":"={{ ($json.output.indigenous_average_rating - $json.average_rating) / $json.std_dev }}"},{"id":"7c6a8d38-02b7-47a1-af44-5eebfb4140ec","name":"population_analysis.effect_sizes.male","type":"number","value":"={{ ($json.output.male_average_rating - $json.average_rating) / $json.std_dev }}"},{"id":"4bf3dba9-4d07-4315-83ce-5fba288a00c9","name":"population_analysis.effect_sizes.female","type":"number","value":"={{ ($json.output.female_average_rating - $json.average_rating) / $json.std_dev }}"},{"id":"d5e980b8-d7a8-4d4c-bcd9-fd9cbd20c729","name":"population_analysis.effect_sizes.trans","type":"number","value":"={{ ($json.output.trans_average_rating - $json.average_rating) / $json.std_dev }}"},{"id":"2c8271c1-b612-4292-9d48-92c342b83727","name":"population_analysis.effect_sizes.hetero","type":"number","value":"={{ ($json.output.hetero_average_rating - $json.average_rating) / $json.std_dev }}"},{"id":"996f2ea0-2e46-424b-9797-2d58fd56b1d3","name":"population_analysis.effect_sizes.lgbtqia","type":"number","value":"={{ ($json.output.lgbtqia_average_rating - $json.average_rating) / $json.std_dev }}"},{"id":"8c987b6e-764d-422e-82de-00bd89269b22","name":"population_analysis.effect_sizes.nondisabled","type":"number","value":"={{ ($json.output.nondisabled_average_rating - $json.average_rating) / $json.std_dev }}"},{"id":"ab796bb7-06ff-4282-b4b3-eefd129c743e","name":"population_analysis.effect_sizes.disabled","type":"number","value":"={{ ($json.output.disabled_average_rating - $json.average_rating) / $json.std_dev }}"},{"id":"a17bf413-a098-4f24-8162-821a6a0ddb5e","name":"population_analysis.effect_sizes.caregiver","type":"number","value":"={{ ($json.output.caregiver_average_rating - $json.average_rating) / $json.std_dev }}"},{"id":"99911e1e-06e8-4bbd-915d-b92b8b37b374","name":"population_analysis.effect_sizes.parent","type":"number","value":"={{ ($json.output.parent_average_rating - $json.average_rating) / $json.std_dev }}"},{"id":"4ddf729b-361e-4d81-a67c-b6c18509e60b","name":"population_analysis.effect_sizes.nonparent","type":"number","value":"={{ ($json.output.nonparent_average_rating - $json.average_rating) / $json.std_dev }}"},{"id":"725b8abb-7f72-45fc-a0c0-0e0a4f2cb131","name":"population_analysis.effect_sizes.nonveteran","type":"number","value":"={{ ($json.output.nonveteran_average_rating - $json.average_rating) / $json.std_dev }}"},{"id":"20e54fa5-2faa-4134-90e5-81224ec9659e","name":"population_analysis.effect_sizes.veteran","type":"number","value":"={{ ($json.output.veteran_average_rating - $json.average_rating) / $json.std_dev }}"},{"id":"2cc6465a-3a1c-4eb5-9e5a-72d41049d81e","name":"population_analysis.review_count.asian","type":"number","value":"={{ $json.output.asian_total_number_of_reviews }}"},{"id":"0a5f6aae-ba21-47b5-8af8-fec2256e4df6","name":"population_analysis.review_count.hispanic","type":"number","value":"={{ $json.output.hispanic_total_number_of_reviews }}"},{"id":"ae124587-7e24-4c1a-a002-ed801f859c30","name":"population_analysis.review_count.pacific_islander","type":"number","value":"={{ $json.output.pacific_islander_total_number_of_reviews }}"},{"id":"fc790196-ca8e-4069-a093-87a413ebbf3e","name":"population_analysis.review_count.black","type":"number","value":"={{ $json.output.black_total_number_of_reviews }}"},{"id":"7fd72701-781e-4e33-b000-174a853b172b","name":"population_analysis.review_count.middle_eastern","type":"number","value":"={{ $json.output.middle_eastern_total_number_of_reviews }}"},{"id":"3751e7da-11a7-4af3-8aa6-1c6d53bcf27d","name":"population_analysis.review_count.indigenous","type":"number","value":"={{ $json.output.indigenous_total_number_of_reviews }}"},{"id":"9ee0cac9-d2dd-4ba0-90ee-b2cdd22d9b77","name":"population_analysis.review_count.male","type":"number","value":"={{ $json.output.male_total_number_of_reviews }}"},{"id":"ae7fcdc7-d373-4c24-9a65-94bd2b5847a8","name":"population_analysis.review_count.female","type":"number","value":"={{ $json.output.female_total_number_of_reviews }}"},{"id":"3f53d065-269f-425a-b27d-dc5a3dbb6141","name":"population_analysis.review_count.trans","type":"number","value":"={{ $json.output.trans_total_number_of_reviews 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}}"},{"id":"41084e96-c42f-4bb0-ac1a-883b46537fca","name":"population_analysis.review_count.parent","type":"number","value":"={{ $json.output.parent_total_number_of_reviews }}"},{"id":"96496a38-9311-4ade-bd2f-2943d1d92314","name":"population_analysis.review_count.nonparent","type":"number","value":"={{ $json.output.nonparent_total_number_of_reviews }}"},{"id":"5071771d-5f41-43cb-a8ce-e4e40ed3519b","name":"population_analysis.review_count.nonveteran","type":"number","value":"={{ $json.output.nonveteran_total_number_of_reviews }}"},{"id":"2358e782-70da-4964-b625-5fe1946b5250","name":"population_analysis.review_count.veteran","type":"number","value":"={{ $json.output.veteran_total_number_of_reviews }}"}]}},"typeVersion":3.4},{"id":"85536931-839a-476b-b0dd-fa6d01c6d5c1","name":"Format dataset for scatterplot","type":"n8n-nodes-base.code","position":[3340,760],"parameters":{"jsCode":"// Iterate through the input data and format the dataset for QuickChartnfor (const item of $input.all()) {n // Ensure the data object exists and initialize datasetsn item.json.data = {n datasets: []n };nn const z_scores = item.json.population_analysis.z_scores;n const effect_sizes = item.json.population_analysis.effect_sizes;n const review_count = item.json.population_analysis.review_count;nn // Ensure z_scores, effect_sizes, and review_count are defined and are objectsn if (z_scores && effect_sizes && review_count && typeof z_scores === 'object' && typeof effect_sizes === 'object' && typeof review_count === 'object') {n // Initialize the dataset objectn const dataset = {n label: 'Demographics Data',n data: []n };nn // Iterate through the demographic keysn for (const key in z_scores) {n // Check if review count for the demographic is greater than 0n if (z_scores.hasOwnProperty(key) && effect_sizes.hasOwnProperty(key) && review_count[key] > 0) {nn // Add each demographic point to the datasetn dataset.data.push({n x: z_scores[key], // x = z_scoren y: effect_sizes[key], // y = effect_sizen label: $('Define dictionary of demographic keys').first().json[key],n });n }n }nn // Only add the dataset if it contains datan if (dataset.data.length > 0) {n item.json.data.datasets.push(dataset);n }nn delete item.json.population_analysisn }n}nn// Return the updated input with the data object containing datasets and labelsnreturn $input.all();n"},"typeVersion":2},{"id":"957b9f6c-7cf8-4ec6-aec7-a7d59ed3a4ad","name":"Specify additional parameters for scatterplot","type":"n8n-nodes-base.set","position":[3540,760],"parameters":{"options":{"ignoreConversionErrors":false},"assignments":{"assignments":[{"id":"5cd507f6-6835-4d2e-8329-1b5d24a3fc15","name":"type","type":"string","value":"scatter"},{"id":"80b6f981-e3c7-4c6e-a0a1-f30d028fe15e","name":"options","type":"object","value":"={n "title": {n "display": true,n "position": "top",n "fontSize": 12,n "fontFamily": "sans-serif",n "fontColor": "#666666",n "fontStyle": "bold",n "padding": 10,n "lineHeight": 1.2,n "text": "{{ $('SET company_name').item.json.company_name }} Workplace Population Bias"n },n "legend": {n "display": falsen },n "scales": {n "xAxes": [n {n "scaleLabel": {n "display": true,n "labelString": "Z-Score",n "fontColor": "#666666",n "fontSize": 12,n "fontFamily": "sans-serif"n }n }n ],n "yAxes": [n {n "scaleLabel": {n "display": true,n "labelString": "Effect Score",n "fontColor": "#666666",n "fontSize": 12,n "fontFamily": "sans-serif"n }n }n ]n },n "plugins": {n "datalabels": {n "display": true,n "align": "top",n "anchor": "center",n "backgroundColor": "#eee",n "borderColor": "#ddd",n "borderRadius": 6,n "borderWidth": 1,n "padding": 4,n "color": "#000",n "font": {n "family": "sans-serif",n "size": 10,n "style": "normal"n }n }n }n }"}]},"includeOtherFields":true},"typeVersion":3.4},{"id":"a937132c-43fc-4fa0-ae35-885da89e51d1","name":"Quickchart Scatterplot","type":"n8n-nodes-base.httpRequest","position":[3740,760],"parameters":{"url":"https://quickchart.io/chart","options":{},"sendQuery":true,"queryParameters":{"parameters":[{"name":"c","value":"={{ $json.toJsonString() }}"},{"name":"Content-Type","value":"application/json"},{"name":"encoding","value":"url"}]}},"typeVersion":4.2},{"id":"ede1931e-bac8-4279-b3a7-5980a190e324","name":"QuickChart Bar Chart","type":"n8n-nodes-base.quickChart","position":[3740,560],"parameters":{"data":"={{ $json.population_analysis.effect_sizes.values() }}","output":"bar_chart","labelsMode":"array","labelsArray":"={{ $json.population_analysis.effect_sizes.keys() }}","chartOptions":{"format":"png"},"datasetOptions":{"label":"={{ $('SET company_name').item.json.company_name }} Effect Size on Employee Experience"}},"typeVersion":1},{"id":"6122fec0-619c-48d3-ad2c-05ed55ba2275","name":"Sticky Note","type":"n8n-nodes-base.stickyNote","position":[480,40],"parameters":{"color":7,"width":3741.593083126444,"height":1044.8111554136713,"content":"# Spot Workplace Discrimination Patterns using ScrapingBee, Glassdoor, OpenAI, and QuickChartn"},"typeVersion":1},{"id":"5cda63e8-f31b-46f6-8cb2-41d1856ac537","name":"Sticky Note1","type":"n8n-nodes-base.stickyNote","position":[900,180],"parameters":{"color":4,"width":1237.3377621763516,"height":575.9439659309116,"content":"## Use ScrapingBee to gather raw data from Glassdoor"},"typeVersion":1},{"id":"28d247b2-9020-4280-83d2-d6583622c0b7","name":"Sticky Note2","type":"n8n-nodes-base.stickyNote","position":[920,240],"parameters":{"color":7,"width":804.3951263154196,"height":125.73173301324687,"content":"### Due to javascript restrictions, a normal HTTP request cannot be used to gather user-reported details from Glassdoor. nnInstead, [ScrapingBee](https://www.scrapingbee.com/) offers a great tool with a very generous package of free tokens per month, which works out to roughly 4-5 runs of this workflow."},"typeVersion":1},{"id":"d65a239c-06d2-470b-b24a-23ec00a9f148","name":"Sticky Note3","type":"n8n-nodes-base.stickyNote","position":[2180,99.69933502879758],"parameters":{"color":5,"width":311.0523273992095,"height":843.8786512173932,"content":"## Extract details with AI"},"typeVersion":1},{"id":"3cffd188-62a1-43a7-a67f-548e21d2b187","name":"Sticky Note4","type":"n8n-nodes-base.stickyNote","position":[2516.1138215303854,100],"parameters":{"color":7,"width":423.41585047129973,"height":309.71740416262054,"content":"### Calculate variance and standard deviation from review rating distributions."},"typeVersion":1},{"id":"b5015c07-03e3-47d4-9469-e831b2c755c0","name":"Sticky Note6","type":"n8n-nodes-base.stickyNote","position":[3320,706.46982689582],"parameters":{"color":5,"width":639.5579220386832,"height":242.80759628871897,"content":"## Formatting datasets for Scatterplot"},"typeVersion":1},{"id":"e52bb9d9-617a-46f5-b217-a6f670b6714c","name":"Sticky Note7","type":"n8n-nodes-base.stickyNote","position":[500,120],"parameters":{"width":356.84794255678776,"height":186.36110628732342,"content":"## How this workflow worksn1. Replace ScrapingBee and OpenAI credentialsn2. Replace company_name with company of choice (workflow performs better with larger US-based organizations)n3. Preview QuickChart data visualizations and AI data analysis"},"typeVersion":1},{"id":"d83c07a3-04ed-418f-94f1-e70828cba8b2","name":"Sticky Note8","type":"n8n-nodes-base.stickyNote","position":[500,880],"parameters":{"color":6,"width":356.84794255678776,"height":181.54335665904924,"content":"### Inspired by [Wes Medford's Medium Post](https://medium.com/@wryanmedford/an-open-letter-to-twilios-leadership-f06f661ecfb4)nnWes performed the initial data analysis highlighting problematic behaviors at Twilio. I wanted to try and democratize the data analysis they performed for those less technical.nn**Hi, Wes!**"},"typeVersion":1},{"id":"ed0c1b4a-99fe-4a27-90bb-ac38dd20810b","name":"Sticky Note9","type":"n8n-nodes-base.stickyNote","position":[4020,880],"parameters":{"color":7,"width":847.5931795867759,"height":522.346478008115,"content":"![image](https://quickchart.io/chart?c=%7B%0A%20%20%22type%22%3A%20%22scatter%22%2C%0A%20%20%22data%22%3A%20%7B%0A%20%20%20%20%22datasets%22%3A%20%5B%0A%20%20%20%20%20%20%7B%0A%20%20%20%20%20%20%20%20%22label%22%3A%20%22Demographics%20Data%22%2C%0A%20%20%20%20%20%20%20%20%22data%22%3A%20%5B%0A%20%20%20%20%20%20%20%20%20%20%7B%0A%20%20%20%20%20%20%20%20%20%20%20%20%22x%22%3A%201.1786657494327952%2C%0A%20%20%20%20%20%20%20%20%20%20%20%20%22y%22%3A%200.16190219204909295%0A%20%20%20%20%20%20%20%20%20%20%7D%2C%0A%20%20%20%20%20%20%20%20%20%20%7B%0A%20%20%20%20%20%20%20%20%20%20%20%20%22x%22%3A%200.5119796850491362%2C%0A%20%20%20%20%20%20%20%20%20%20%20%20%22y%22%3A%200.0809510960245463%0A%20%20%20%20%20%20%20%20%20%20%7D%2C%0A%20%20%20%20%20%20%20%20%20%20%7B%0A%20%20%20%20%20%20%20%20%20%20%20%20%22x%22%3A%20-0.9300572848378476%2C%0A%20%20%20%20%20%20%20%20%20%20%20%20%22y%22%3A%20-0.16190219204909329%0A%20%20%20%20%20%20%20%20%20%20%7D%2C%0A%20%20%20%20%20%20%20%20%20%20%7B%0A%20%20%20%20%20%20%20%20%20%20%20%20%22x%22%3A%20-0.42835293687811976%2C%0A%20%20%20%20%20%20%20%20%20%20%20%20%22y%22%3A%20-0.16190219204909329%0A%20%20%20%20%20%20%20%20%20%20%7D%2C%0A%20%20%20%20%20%20%20%20%20%20%7B%0A%20%20%20%20%20%20%20%20%20%20%20%20%22x%22%3A%20-1.0890856121128139%2C%0A%20%20%20%20%20%20%20%20%20%20%20%20%22y%22%3A%20-0.08095109602454664%0A%20%20%20%20%20%20%20%20%20%20%7D%2C%0A%20%20%20%20%20%20%20%20%20%20%7B%0A%20%20%20%20%20%20%20%20%20%20%20%20%22x%22%3A%20-1.7362075843299012%2C%0A%20%20%20%20%20%20%20%20%20%20%20%20%22y%22%3A%20-0.16190219204909329%0A%20%20%20%20%20%20%20%20%20%20%7D%2C%0A%20%20%20%20%20%20%20%20%20%20%7B%0A%20%20%20%20%20%20%20%20%20%20%20%20%22x%22%3A%20-2.9142394568836774%2C%0A%20%20%20%20%20%20%20%20%20%20%20%20%22y%22%3A%20-0.971413152294559%0A%20%20%20%20%20%20%20%20%20%20%7D%2C%0A%20%20%20%20%20%20%20%20%20%20%7B%0A%20%20%20%20%20%20%20%20%20%20%20%20%22x%22%3A%20-1.2088576542791578%2C%0A%20%20%20%20%20%20%20%20%20%20%20%20%22y%22%3A%20-0.08095109602454664%0A%20%20%20%20%20%20%20%20%20%20%7D%2C%0A%20%20%20%20%20%20%20%20%20%20%7B%0A%20%20%20%20%20%20%20%20%20%20%20%20%22x%22%3A%20-2.5276971632072494%2C%0A%20%20%20%20%20%20%20%20%20%20%20%20%22y%22%3A%20-0.4047554801227329%0A%20%20%20%20%20%20%20%20%20%20%7D%2C%0A%20%20%20%20%20%20%20%20%20%20%7B%0A%20%20%20%20%20%20%20%20%20%20%20%20%22x%22%3A%200%2C%0A%20%20%20%20%20%20%20%20%20%20%20%20%22y%22%3A%200%0A%20%20%20%20%20%20%20%20%20%20%7D%2C%0A%20%20%20%20%20%20%20%20%20%20%7B%0A%20%20%20%20%20%20%20%20%20%20%20%20%22x%22%3A%20-5.504674529669168%2C%0A%20%20%20%20%20%20%20%20%20%20%20%20%22y%22%3A%20-1.376168632417292%0A%20%20%20%20%20%20%20%20%20%20%7D%2C%0A%20%20%20%20%20%20%20%20%20%20%7B%0A%20%20%20%20%20%20%20%20%20%20%20%20%22x%22%3A%20-0.8412684674574105%2C%0A%20%20%20%20%20%20%20%20%20%20%20%20%22y%22%3A%20-0.24285328807363996%0A%20%20%20%20%20%20%20%20%20%20%7D%2C%0A%20%20%20%20%20%20%20%20%20%20%7B%0A%20%20%20%20%20%20%20%20%20%20%20%20%22x%22%3A%20-2.896194457023989%2C%0A%20%20%20%20%20%20%20%20%20%20%20%20%22y%22%3A%20-0.32380438409818657%0A%20%20%20%20%20%20%20%20%20%20%7D%2C%0A%20%20%20%20%20%20%20%20%20%20%7B%0A%20%20%20%20%20%20%20%20%20%20%20%20%22x%22%3A%20-1.0303392409819254%2C%0A%20%20%20%20%20%20%20%20%20%20%20%20%22y%22%3A%20-0.08095109602454664%0A%20%20%20%20%20%20%20%20%20%20%7D%2C%0A%20%20%20%20%20%20%20%20%20%20%7B%0A%20%20%20%20%20%20%20%20%20%20%20%20%22x%22%3A%20-1.2670850749479952%2C%0A%20%20%20%20%20%20%20%20%20%20%20%20%22y%22%3A%20-0.08095109602454664%0A%20%20%20%20%20%20%20%20%20%20%7D%2C%0A%20%20%20%20%20%20%20%20%20%20%7B%0A%20%20%20%20%20%20%20%20%20%20%20%20%22x%22%3A%201.535939055147413%2C%0A%20%20%20%20%20%20%20%20%20%20%20%20%22y%22%3A%200.4857065761472792%0A%20%20%20%20%20%20%20%20%20%20%7D%0A%20%20%20%20%20%20%20%20%5D%0A%20%20%20%20%20%20%7D%0A%20%20%20%20%5D%2C%0A%20%20%20%20%22labels%22%3A%20%5B%0A%20%20%20%20%20%20%22asian%22%2C%0A%20%20%20%20%20%20%22hispanic%22%2C%0A%20%20%20%20%20%20%22black%22%2C%0A%20%20%20%20%20%20%22middle_eastern%22%2C%0A%20%20%20%20%20%20%22male%22%2C%0A%20%20%20%20%20%20%22female%22%2C%0A%20%20%20%20%20%20%22trans%22%2C%0A%20%20%20%20%20%20%22hetero%22%2C%0A%20%20%20%20%20%20%22lgbtqia%22%2C%0A%20%20%20%20%20%20%22nondisabled%22%2C%0A%20%20%20%20%20%20%22disabled%22%2C%0A%20%20%20%20%20%20%22caregiver%22%2C%0A%20%20%20%20%20%20%22parent%22%2C%0A%20%20%20%20%20%20%22nonparent%22%2C%0A%20%20%20%20%20%20%22nonveteran%22%2C%0A%20%20%20%20%20%20%22veteran%22%0A%20%20%20%20%5D%0A%20%20%7D%2C%0A%20%20%22options%22%3A%20%7B%0A%20%20%20%20%22title%22%3A%20%7B%0A%20%20%20%20%20%20%22display%22%3A%20true%2C%0A%20%20%20%20%20%20%22position%22%3A%20%22top%22%2C%0A%20%20%20%20%20%20%22fontSize%22%3A%2012%2C%0A%20%20%20%20%20%20%22fontFamily%22%3A%20%22sans-serif%22%2C%0A%20%20%20%20%20%20%22fontColor%22%3A%20%22%23666666%22%2C%0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Note10","type":"n8n-nodes-base.stickyNote","position":[3980,800],"parameters":{"color":6,"width":989.7621518164046,"height":636.6345107975716,"content":"## Example Scatterplot output"},"typeVersion":1},{"id":"bd6859b4-096c-401e-9bce-91e970e1afd1","name":"Sticky Note11","type":"n8n-nodes-base.stickyNote","position":[2540,800],"parameters":{"color":6,"width":737.6316136259719,"height":444.9087184962878,"content":"## Glossaryn**Z-Score** – A statistical measure that indicates how many standard deviations a data point is from the mean. In this analysis, a negative z-score suggests a group rates their workplace experience lower than the average, while a positive z-score suggests a better-than-average experience.nn**Effect Size** – A measure of the magnitude of difference between groups. Larger negative effect sizes indicate a more substantial disparity in workplace experiences for certain groups, making it useful for identifying meaningful gaps beyond just statistical significance.nn**P-Score (P-Value)** – The probability that the observed differences occurred by chance. A lower p-score (typically below 0.05) suggests the difference is statistically significant and unlikely to be random. In this analysis, high p-scores confirm that the disparities in ratings for marginalized groups are unlikely to be due to chance alone.nn### Relevance to This AnalysisnThese metrics help quantify workplace disparities among demographic groups. Z-scores show which groups report better or worse experiences, effect sizes reveal the severity of these differences, and p-scores confirm whether the disparities are statistically meaningful. This data allows for a more informed discussion about workplace equity and areas needing improvement."},"typeVersion":1},{"id":"5af3ef87-ed4b-481e-b1ba-d44ffb7551d8","name":"Sticky Note12","type":"n8n-nodes-base.stickyNote","position":[4140,80],"parameters":{"color":6,"width":643.5995639515581,"height":646.0030521944287,"content":"## Example AI Analysis (Twilio Example)nn### Key Takeawaysn1. **Significant Disparity Among Disabled Employees**nDisabled employees reported the lowest average ratings, with a z-score of -5.50, indicating a far worse experience compared to their non-disabled peers. n2. **LGBTQIA Community's Challenges**nMembers of the LGBTQIA community showed significantly lower ratings (z-score of -2.53), suggesting they may experience a workplace environment that is less inclusive or supportive compared to others.n3. **Transgender Experiences Are Particularly Negative**nTransgender employees rated their experiences considerably lower (z-score of -2.91), highlighting a critical area for improvement in workplace culture and acceptance.n4. **Veterans Report Higher Satisfaction**nIn contrast, veterans had the highest ratings (z-score of 1.54), which could indicate a supportive environment or programs tailored to their needs.n5. **Overall Gender Discrepancies**nA noticeable gap exists in average ratings by gender, with female employees scoring below male employees, suggesting potential gender biases or challenges in workplace dynamics.nn### Employee Experiencesn#### Perceptions of Workplace EnvironmentnFor members of groups reporting significantly worse experiences, such as disabled, transgender, and LGBTQIA employees, the workplace may feel alienating or unwelcoming. These individuals might perceive that their contributions are undervalued or overlooked and that necessary support systems are lacking, creating a culture of exclusion rather than one of inclusivity. This feeling of being marginalized can lead to poorer engagement, higher turnover rates, and diminished overall job satisfaction, adversely impacting both employees and the organization."},"typeVersion":1},{"id":"a39cdbe7-d6ae-4a84-98c7-52ebf98242f3","name":"Text Analysis of Bias Data","type":"@n8n/n8n-nodes-langchain.chainLlm","position":[3720,280],"parameters":{"text":"=This data compares the average rating given by different demographic groups against a baseline (the overall mean rating).nnObjective:n1. Analyze the data and offer between 2 and 5 key takeaways with a title and short (one-sentence) summary.n2. Below the key takeaways, Include a heading called "Employee Experiences". Under this heading, include a subheader and paragraph describing the possible perception of the workplace for members of any groups reporting significantly worse (or better) experiences than others.n3. Ensure there are between 2-5 key takeaways and employee experiencesnnData for analysis:n{{ $json.population_analysis.toJsonString() }}","promptType":"define"},"typeVersion":1.4}],"active":false,"pinData":{},"settings":{"executionOrder":"v1"},"versionId":"ff1df786-ebaf-4ed0-aeca-1872b93ef275","connections":{"Merge":{"main":[[{"node":"Calculate Z-Scores and Effect Sizes","type":"main","index":0}]]},"SET company_name":{"main":[[{"node":"Define dictionary of demographic keys","type":"main","index":0}]]},"OpenAI Chat Model":{"ai_languageModel":[[{"node":"Text Analysis of Bias Data","type":"ai_languageModel","index":0}]]},"Sort Effect Sizes":{"main":[[{"node":"QuickChart Bar Chart","type":"main","index":0},{"node":"Text Analysis of Bias Data","type":"main","index":0}]]},"Calculate P-Scores":{"main":[[{"node":"Sort Effect Sizes","type":"main","index":0}]]},"OpenAI Chat Model1":{"ai_languageModel":[[{"node":"Extract demographic distributions","type":"ai_languageModel","index":0}]]},"OpenAI Chat Model2":{"ai_languageModel":[[{"node":"Extract overall ratings and distribution percentages","type":"ai_languageModel","index":0}]]},"Extract company url path":{"main":[[{"node":"ScrapingBee GET company page contents","type":"main","index":0}]]},"Set variance and std_dev":{"main":[[{"node":"Merge","type":"main","index":0}]]},"Extract Demographics Module":{"main":[[{"node":"Extract demographic distributions","type":"main","index":0}]]},"ScrapingBee Search Glassdoor":{"main":[[{"node":"Extract company url path","type":"main","index":0}]]},"Extract reviews page url path":{"main":[[{"node":"ScrapingBee GET Glassdoor Reviews Content","type":"main","index":0}]]},"Extract Overall Review Summary":{"main":[[{"node":"Extract overall ratings and distribution percentages","type":"main","index":0}]]},"Format dataset for scatterplot":{"main":[[{"node":"Specify additional parameters for scatterplot","type":"main","index":0}]]},"Define contributions to variance":{"main":[[{"node":"Set variance and std_dev","type":"main","index":0}]]},"Extract demographic distributions":{"main":[[{"node":"Merge","type":"main","index":1}]]},"When clicking ‘Test workflow’":{"main":[[{"node":"SET company_name","type":"main","index":0}]]},"Calculate Z-Scores and Effect Sizes":{"main":[[{"node":"Calculate P-Scores","type":"main","index":0},{"node":"Format dataset for scatterplot","type":"main","index":0}]]},"Define dictionary of demographic keys":{"main":[[{"node":"ScrapingBee Search Glassdoor","type":"main","index":0}]]},"ScrapingBee GET company page contents":{"main":[[{"node":"Extract reviews page url path","type":"main","index":0}]]},"ScrapingBee GET Glassdoor Reviews Content":{"main":[[{"node":"Extract Demographics Module","type":"main","index":0},{"node":"Extract Overall Review Summary","type":"main","index":0}]]},"Specify additional parameters for scatterplot":{"main":[[{"node":"Quickchart Scatterplot","type":"main","index":0}]]},"Extract overall ratings and distribution percentages":{"main":[[{"node":"Define contributions to variance","type":"main","index":0}]]}}}
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