How to … bring a JEDI (justice, equity, diversity and inclusion) lens to your research
Bibliographic record
Abstract
How does my background (racial, cultural, socio-economic, disciplinary, etc.) influence what I emphasise in this research?How might community members' perspectives help me understand the data in a new or different way?How do different perspectives challenge my assumptions and disconfirm my insights?How might insights based on various perspectives contextualise participants' experiences into structural inequality?How do my racialised and cultural backgrounds influence what I perceive as 'emerging' in these data?Is there a strand of critical theory I can use to better understand the role of bias and oppression in these data?26,27 What larger social, political, economic and historical forces shape participants' experiences?ORCID
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.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.100 | 0.152 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.018 | 0.061 |
| Scholarly communication | 0.038 | 0.042 |
| Open science | 0.004 | 0.020 |
| Research integrity | 0.015 | 0.031 |
| Insufficient payload (model declined to judge) | 0.009 | 0.006 |
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".