Bibliographic record
Abstract
Gender Realities: Local and Global: Introduction. (V. Demos, M. Texler Segal). Gender Segregation in the Hidden Labor Force: Looking at the Relationship Between the Formal and Informal Economies. (K.A. Snyder). Resettlement and Risk: Women's Community Work in Lesotho. (Y.A. Braun). Lessons From India: Applying a 'Third World' Framework to Examine the Impacts of Fisheries Crisis on Women in Newfoundland Villages. (D. Harrison, N. Gerarda Power). Gender, Economic Development and the Puerto Rican Welfare State. (M. Morrissey). Dual-Earner Couples' Expectations for Joint Retirement: A Study of Typical and atypical Congruent and Non-Congruent Couples. (A. Behringer, C.C. Perrucci, R. Hogan). The Globalization of Sexual Harassment. (J. Markert). Women's Work-Related and Family-Related Discrimination and Support in Academia. (L. Husu). Working Much Harder and Always Having to Prove Yourself: Immigrant Women's Labor Force Experiences in the Canadian Maritimes. (E. Tastsoglou, B. Miedema). Shooting the Messenger and the Message: The Social Basis of Authority Challenges in Canadian Law School Settings. (A. Nierobisz, J. Hagan). Processes of Gendering and the Institutionalization of Gender in the Family and School: A Case Study from Nepal. (J. Rothchild).
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.006 | 0.010 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.040 | 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".