Discreet Openness: Scholars’ Selective and Intentional Self-Disclosures Online
Bibliographic record
Abstract
Research into emergent forms of scholarship focuses on academics’ use of technology for learning, teaching, and research. Very little attention has been paid in the literature to scholars’ uses of social media to disclose challenging personal and professional issues. This article addresses the identified gap in the literature and presents a qualitative investigation into the types of disclosures that 16 scholars made online and their reasons for doing so. Results identify wide-ranging personal and professional disclosures. Participants disclosed not only about academia-related issues but also about challenges pertaining to family, mental health, physical health, identity, and relationships. Some scholars disclosed as a way to grapple with challenges they faced; others disclosed tactically, sharing information for political rather than personal reasons. Yet others disclosed as a way to welcome care in their lives. In all instances, though, disclosures were selective, intentional, and approached with foresight.
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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 | 0.038 | 0.132 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.010 | 0.027 |
| Scholarly communication | 0.013 | 0.014 |
| Open science | 0.002 | 0.019 |
| Research integrity | 0.003 | 0.005 |
| 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".