Bibliographic record
Abstract
In developing the call for papers for the 7th International Federation of Information Processors (IFIP) Women, Work and Computerization Conference, we sought to cast our net widely. We wanted to encourage presenters to think broadly about women, work and computerization. Towards this end, the programme committee developed a call for papers that, in its final form, requested paper submissions around four related themes. These are (1) Setting the Course: Taking Stock of Where We Are and Where We’re Going; (2) Charting Undiscovered Terrain: Creating Models, Tools and Theories; (3) Navigating the Unknown: Sex, Time, Space and Place, and (4) Taking the Helm: Education and Pedagogy. Our overall conference theme, ‘Charting a Course to the Future’ was inspired in part by Vancouver’s geography, which is both coastal and mountainous. As such, navigation plays an important part in the lives of many as we seek to enjoy our environs. In addition, as the first Women, Work and Computerization conference of the new millennium, we hoped to encourage the broad community of scholars that has made past Women, Work and Computerization conferences a success to actively engage in imagining— and working towards— a better future for women in relation to computers. The contributions to this volume are both a reflection of the hard work undertaken by many to improve the situation of women in relation to computerization, and a testament to how much work is yet to be done. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.435 | 0.308 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".