Bibliographic record
Abstract
Purpose The purpose of this paper is to provide insight into how a constellation of actors seek to define, shape, and reinvent the notion of organizational control at the confluence of social media (SM) and corporate reputational risk. Design/methodology/approach Following the approach suggested by Janesick (1998) and Denzin and Lincoln (1998), the authors undertook an in-depth qualitative analysis of a large number of data sources including interviews, best-selling books by renowned SM specialists, relevant press articles drawn from a Factiva search, and documents published by the Big Four firms and professional accounting institutes in Canada on how organizations should use SM to protect their reputational capital. Findings Four competing SM reputational risk control perspectives inductively emerged from the analysis: the Beyond Control frame, the Subveillance frame, the De-territorialization frame, and the Re-territorialization frame, with large accounting firms and professional accounting institutes especially promoting the latter. Originality/value The control literature has been criticized by many scholars as being in urgent need of updating. By inductively theorizing four original control frames in the SM arena, the research aims to move management control research in new directions.
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 | 0.027 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.005 | 0.059 |
| Scholarly communication | 0.011 | 0.009 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.002 | 0.003 |
| 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".