Twittering change: The institutional work of domain change in accounting expertise
Bibliographic record
Abstract
This paper develops an endogenous model of institutional and professional domain change. Traditional accounts of domain change focus attention on how professional expertise is extended to new areas of practice. This form of domain extension is typically both deliberate and contested. However, domain change can also occur in a somewhat quotidian and uncontested fashion when professional expertise is extended intra-organizationally. We analyze the ways in which the domain of accounting expertise is reconstituted in new social media – Facebook, LinkedIn and Twitter – in Big 4 accounting firms. Using content analysis and interview data we show how social media professionals, in pursuing their own professional project, generate change in the professional domain of accountancy. Our analysis demonstrates that the institutional work of domain change occurs through three related activities: boundary work, rhetorical work and the construction of the embedded actor.
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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 | 0.009 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.007 | 0.029 |
| Scholarly communication | 0.011 | 0.017 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".