Bibliographic record
Abstract
For women, sharing space, being acknowledged in that space, is a battle of trust and spirit. Academic spaces have previously been colonised, either by the leader in charge, or a previous ‘owner’ of that space. This presentation and paper describes three common intersectional narratives of Williams’ (1991) ‘spirit murder’; and the ‘protectors and restorers’ (Revilla, 2021) of our space in the academy. Women are battling to occupy their work spaces on a daily basis: trying to speak, teach, research in ways they have organically invented and conceived in their service. They are the caretakers of diverse and different languages, and innovative methods to research and teachings; yet these divergent pathways are often blocked by colonised, ‘expected’ practices within the institution. Even the physical grey and white walls are concrete signs of ownership. The Academy is a place we are in; where we have a right to be in (Sefa Dei, 2021). Trusting and accepting these unique differences is at the heart of moving forward to a more inclusive, rich research and teaching space. Just because a pathway or method is distinct, does not mean it is deficient. For educational leaders, acknowledging shared ownership in the academic space involves sharing and projecting one’s self into the space (Kreger,1999); trusting that, while an approach is unknown, can be successful. To redress space in the academy, educators must be at ease with the discomfort of sharing space in what has yet to be experienced and institutionalised.
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 | 0.012 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.034 | 0.077 |
| Scholarly communication | 0.031 | 0.027 |
| Open science | 0.003 | 0.039 |
| Research integrity | 0.006 | 0.012 |
| Insufficient payload (model declined to judge) | 0.028 | 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".