Cultivating intersectional equality policies and practices in R&I
Bibliographic record
Abstract
This Second Working Paper of the INSPIRE Knowledge and Support Hub (KSH) 3 on Intersectionality is structured into three main sections. The first section introduces the topic of intersectional equality policies and practices. It reflects on the existing scientific literature on intersectional policies in Higher Education and Research (HE&R) organizations to argue for a more explicitly strategic-political approach to intersectionality in organizational policy-making, and proposes guiding principles for its adoption. This section is based on a presentation given by Patrizia Zanoni on 27 September 2024 at the second Knowledge Exchange Event (KKE) of the INSPIRE project in Vienna, Austria. The second section presents the outcomes of the knowledge exchange among the KKE participants. This included two breakout room sessionsthat focused on how intersectional equality practices and policies can be cultivated. As these outcomes are based on the shared knowledge and expertise of the participants, we recognize their contributions by including the names and the respective institutions of those accepted to be acknowledged. These sessions were developed and facilitated by Lorena Pajares Sánchez (Notus) and Joanna Beeckmans (University of Hasselt). The third section presents the reflections of KSH3 experts Ashlee Christoffersen (York University), Barbara De Micheli (Fondazione Giacomo Brodolini), Bruna Jaquetto Pereira (Universidad Complutense de Madrid) and Irina Lungu (Technical University of Iasi). Moreover, CoP representatives have provided feedback and added their reflections, based on their work within their respective CoPs. The final version of this Second Working Paper benefited from feedback provided by representatives from each CoP, as well as their reflections based on their CoP work conducted within INSPIRE so far.
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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 | 0.081 | 0.063 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.013 | 0.034 |
| Scholarly communication | 0.030 | 0.029 |
| Open science | 0.004 | 0.054 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".