Bibliographic record
Abstract
Lynn Butler-Kisber, McGill University Mary Stewart, LEARN Quebec The Importance of Care in the Publishing Process In this paper we will highlight with stories how, when care is integrated into the various steps in the publication process of qualitative work, it creates a thoughtful dialogue, enhances the ultimate product and scaffolds learning about both content and methodology, without sacrificing quality. Much has been written about the importance of the care in educational contexts (Noddings, 2005). Care refers to “a set of relational processes that foster mutual recognition and realization, growth, development, protection, empowerment, and human community, culture and possibility”—in learning situations (Owens, Ennis, 2005, p. 392). However, little attention has focused on the role that care can play in the publication process. Publications are frequently tied to outcomes such as promotion and tenure with little consideration about how the actual process can contribute to the development of both practitioner and academic authors and their participants. Care in the publication process contributes to how qualitative research gets delivered accessibly, transparently, and poignantly. References Noddings, N. (2005). The challenge to care in the schools. New York: Teachers’ College Press. Owens, L. M., & Ennis, K. D. (2005). The ethic of care in teaching: An overview of supportive literature. QUEST, 57, 392-425.
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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 | 0.092 | 0.174 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.035 | 0.036 |
| Scholarly communication | 0.038 | 0.017 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.009 | 0.014 |
| Insufficient payload (model declined to judge) | 0.010 | 0.004 |
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".