Bibliographic record
Abstract
Introduction We began our discussion of corporate identity as an integral element of corporate culture back in Chapter 4, as we set out the step-by-step model for social strategy formulation and implementation. Step 3, you may recall, is “Evaluate firm identity.” In Chapter 4, we dodged the multiple issues of defining a workable concept of culture, which we must try to make amends for here. While management unflinchingly invokes culture as the reason for doing things in a certain way, and “our culture” is often said to explain what makes the company what it is, culture is a highly contested concept in academics. First, the concept is properly in the domain of anthropology – in fact, so central to the field that it is often simply called cultural anthropology, and so divisive that anthropology habitually engages in definitional turf wars over the term (Geertz, 1973). Cultural anthropology, like its sister science, sociology, has a habit of bumping into psychology and borrows much of its terminology from early twentieth-century work in personality psychology in areas such as identity and values. The concepts of culture, identity, and values are dragged into management research and applied to organizations with questionable results. To be fair to the management field, and the social sciences in general, these concepts are difficult, highly abstract, yet fundamental to all human behavior.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.008 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.015 | 0.004 |
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".