Bibliographic record
Abstract
Coordinating in organizations is not an easy endeavor. Research has identified a wide variety of formal and informal coordinating mechanisms that support organizational work (Faraj & Xiao, 2006; Okhuysen & Bechky, 2009), yet we still know little about how coordinating is achieved in practice. This manuscript introduces posits dialogical presentation practices (DPP) as a fundamental element of coordinating processes. Framing the discussion within the setting on a Neonatal Intensive Care Unit, I show how DPPs and processes incorporating them allow individuals to build a common understanding of the past, project it in the future, and delineate actions in the process to connect the two. Through this process, individuals make themselves accountable for past action and update predictability, either by confirming current practices that work, or by learning from discrepancies, and updating or changing practices. Through their practices, they thus construct the conditions for coordination (common understanding, accountability, and predictability, Okhuysen & Bechky, 2009).
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 | 0.036 | 0.066 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.008 | 0.012 |
| Scholarly communication | 0.013 | 0.017 |
| Open science | 0.003 | 0.015 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".