Occupational image, organizational image and identity in dirty work: Intersections of organizational efforts and media accounts
Bibliographic record
Abstract
This article proposes that media representations of an occupational category may intersect with organizations’ efforts to construct a positive organizational identity and image. We fuse three streams of literature namely, organizational identity and image, media and the social construction of reality, and dirty work to extend extant literature on organizational identity and image. Attention is drawn to occupational image as the position of an occupational category in society. We contend that occupational image is likely to influence the decisions and actions taken by organizations and its members, in particular when the occupation is central to the organization’s mission. Occupational image is partly informed by the media. We analyse one year of media coverage of a dirty work occupation, specifically exotic dancing, and identify various ways in which the media portrays the exotic dancing occupation and the organizations providing these services. We focus upon two of these categories, namely Public (dis) Order and Art and Entertainment. We also draw upon a variety of data from one organization, For Your Eyes Only, to explore how organizational efforts to construct a positive organizational identity (based upon professionalism and legitimacy) and image (based upon fantasy, exclusivity and high quality service) intersect these media representations.
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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.005 | 0.010 |
| Scholarly communication | 0.011 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".