Inclusion and protection in tension: Reflections on gathering sexual orientation and gender identity data in the workplace
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract This article addresses the complex issue of sexual orientation and gender identity (SOGI) data collection in workplaces, highlighting the intricate balance between fostering inclusion and mitigating potential harm and exclusion. This tension manifests uniquely across diverse cultural, legal, and organizational settings. We review existing literature, offer practical guidance for decision‐makers, and outline future research avenues. While SOGI data collection in workplaces can enhance diversity, equity, and inclusion (DEI) initiatives and elevate the visibility of lesbian, gay, bisexual, transgender, intersex, and queer (LGBTIQ+) employees, challenges include the risk of discrimination, privacy concerns, and linguistic complexities. To address these, researchers and practitioners must consider the purpose, language, and cultural context of data collection, involving LGBTIQ+ stakeholders, and conducting reconnaissance studies. Future research opportunities lie in understanding employee willingness to share SOGI data, motivations of human resource (HR) and DEI professionals, and the impact on organizational culture. Reimagining LGBTIQ+ research to ease the tension between inclusion and protection, we conclude that responsible SOGI data collection demands a nuanced approach that prioritizes inclusion and equity while addressing privacy concerns and potential harm.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it