Ready? Set... Go! Create Your Own Job The Development of an Entrepreneurship Training Program
Bibliographic record
Abstract
particuhcspour h fmmes. D iiprts ks autnrrcs, ksfimmcs doivmt avoir acc2s h une finnation adlquate pour h e cn mesure &finder kurpropre mtnprisc et ainsi contourner h The emphasis in my jo b shifed fiom being a service provider, a link with the women 5 community, an educationalprogrammer, and a researcher to becoming financially self-supporting. probhes qui stprCsmtmt lors de la rccherche d'nnploi. Cet artick retrace I'tvolution d'un programme defirmation de quatrc jours ainsi gut la rCacttctron dr ses participantes. With the recession, cut-backs in health, social services, education, and the transformation of the economy through new information technologies, many workplaces tradi-tionally held by women have been lost in Canada. In an article in The GlobeandMaid Margaret Wente put a now
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.544 | 0.404 |
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".