NSERC CHAIR FOR WOMEN IN SCIENCE & ENGINEERING PROGRAM – STRATEGIC DIRECTIONS FOR WISE INITIATIVES
Bibliographic record
Abstract
At the 12th National CCWESTT Conference: Building on Success, there was a workshop to solicit ideas from conference participants on strategic directions for promoting the participation of women in SETT. The focus of the workshop was to discuss how the NSERC Chair for Women in Science and Engineering (CWSE) program can best support the work of relevant stakeholders and to identify what support and resources may be available from stakeholders to enable the CWSE program to function better. \n \nThe session was held on the final day of the conference (31st of May 2008) and was attended by approximately 50 women who had participated in the conference. These women represented various interest groups (stakeholders) including: undergraduate and graduate students in science and engineering, social science researchers/academics, professional engineers and scientists, and those working in advocacy programs. The 5 current NSERC chairs representing (1) the Atlantic provinces, (2) British Columbia & Yukon, (3) Ontario, (4) the Prairie provinces, and (5) Quebec, were also in attendance. \nThe workshop participants were asked to grapple with two questions: \n1)\tHow can the NSERC Chairs Programs make a meaningful contribution to the success of your work? \n2)\tHow can your work make a meaningful contribution to the NSERC Chairs Program? \n \nParticipants were encouraged to contemplate the questions with their specific interest group (e.g., student, researcher) in mind. The participants were assigned to 4 different groups. Three groups were asked to focus on question one and the fourth group was asked to focus on second question. Under the guidance of a facilitator, the groups held discussions and generated responses to the questions. Individual participants also responded to the questions on paper. This report summarizes the ideas generated from both groups and individuals during the session.
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 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 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".