EMOTIONS AND SELF-CULTIVATION IN<i>NÜ LUNYU</i>«女論語» (WOMAN'S<i>ANALECTS</i>)
Bibliographic record
Abstract
Journal of Chinese PhilosophyVolume 36, Issue 2 p. 334-347 EMOTIONS AND SELF-CULTIVATION IN NÜ LUNYU«???» (WOMAN'S ANALECTS) TERRY TAK-LING WOO, Corresponding Author TERRY TAK-LING WOO Toronto, CanadaTERRY TAK-LING WOO, Ph.D., currently Independent Scholar. Specialties: women in Chinese religions, globalization and diaspora. E-mail: [email protected]Search for more papers by this author TERRY TAK-LING WOO, Corresponding Author TERRY TAK-LING WOO Toronto, CanadaTERRY TAK-LING WOO, Ph.D., currently Independent Scholar. Specialties: women in Chinese religions, globalization and diaspora. E-mail: [email protected]Search for more papers by this author First published: 05 May 2009 https://doi.org/10.1111/j.1540-6253.2009.01522.xCitations: 3Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onEmailFacebookTwitterLinkedInRedditWechat Citing Literature Volume36, Issue2June 2009Pages 334-347 RelatedInformation
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".