"I feel like a bag lady": Personal Interstices, Self-Disclosures and Empathetic Affiliation during Workplace Meetings
Bibliographic record
Abstract
While an extensive body of research exploring professional discourse exists, research investigating off-task talk within workplaces has been relatively side-lined. To better understand the possible functions of personal interstices layered between institutional goal-oriented talk, this study examines instances of self-disclosure that emerged from 34 hours of authentic interactions recorded at three Canadian workplaces. Using conversation analysis, 87 self-reference, self-disclosure declaratives were identified. Of those, 21 occurred within reciprocal sequences between two participants. Similar to a second story telling found in ordinary conversation (Sacks, 1992a), the second speaker’s self-disclosure reflects the first speaker’s, both in content and form, and is often an upgraded version of the initial disclosure. This pattern and in-meeting placement suggest that these types of personal interstices may be a mechanism for displaying co-worker empathetic affiliation. Additionally, hierarchical role relations and institution goals may be temporarily suspended or back-grounded during these sequences.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.007 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".