Unsettling Settled Knowing: Reconciling Differences in Expert Practice
Bibliographic record
Abstract
Occupational subgroups with similar training often develop differing work practices within their local settings. These differences may create inconsistencies when organizational change brings subgroups together to work alongside each other. Based on a two-year qualitative study of a hospital merger combining two neonatal intensive care units, we consider how differing expert practices may be challenged, preserved, or reconciled when subgroups are brought together. We find that reconciliation processes are unexpectedly triggered by novice newcomers who struggle to socialize into a consistent way of working. Comparing five expert practices over time, we also find that when groups are able to converge around the type of knowledge that should apply to expert practices (abstract versus experiential knowledge), a form of reconciliation is possible, but when there is divergence around the type of knowledge that is relevant to the situation, reconciliation fails. Converging on experiential knowledge implies a simplified process of reconciliation that preserves expert autonomy while masking residual differences. Converging on abstract knowledge involves a complex, multilayered process in which expert subgroups need to revert in part to mechanisms resembling those that underpinned their initial socialization into the discipline. These mechanisms include mobilizing evidence to update abstract knowledge, situated mentoring with respected experts, and authoritative reinforcing via interventions from high-status professionals. Our study highlights the challenges of changing expert practices that are rooted in ingrained experiential knowledge. It reveals that abstract knowledge alone is insufficient and that reconciliation invariably involves settlements and agreements on what form of knowledge matters. Funding: This work was supported by the Social Sciences and Humanities Research Council of Canada.
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 | 0.040 | 0.080 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.008 | 0.021 |
| Scholarly communication | 0.010 | 0.015 |
| Open science | 0.003 | 0.014 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".