Bibliographic record
Abstract
This volume began as a project of the National Network on En vironments and Women's Health (NNEWH) in 2008-9.Many of the chapters were initially written at that time by participants in a public symposium on "Consuming Chemicals" hosted by York University and a policy forum held in Ottawa with representatives from Health Canada and other relevant audiences.We are indebted to all of the students, policy makers, and academics who attended these events.The NNEWH was funded at that time through the Women's Health Contribution Program of Health Canada.Although the initial policy workshop that led to this volume was thus made possible through a financial contribution from Health Canada, the views expressed here do not necessarily represent those of Health Canada.York University students have been engaged throughout.Lauren Rakowski provided comprehensive editing assistance at an early stage; Adrian Roomes contributed expert research assistance; Sarah Lewis spear headed the process by which the chapters were updated and brought into renewed conversation with each other in 2012, and Vanessa Scanga ably brought us to the final stretches.Ellen Sweeney and Sarah Wiebe were doctoral students I worked with during this period -Ellen's work ethic motivated me, and Sarah's creativity inspired me.Jyoti Phartiyal has been the soul of NNEWH since I've known it, and she is an absolute wonder at what she does.My collaboration with the Health and Environment Committee of Aamjiwnaang First Nation continued throughout the work on this
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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.401 | 0.220 |
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".