Bibliographic record
Abstract
Textbooks are an important element in teaching management in higher education because of their assumed ability to disseminate key theories and debates in a seemingly objective fashion. However, a number of studies have questioned not only the scientific character of the textbook but also of management theory itself. More recent studies suggest that dominant notions of management theory in North America were shaped by the Cold War context. In this article, we examine the influence of sociopolitical context on the development of management textbooks in North America. In seeking a more nuanced approach to sociopolitical context that takes into account cross-cultural differences, we undertook a critical hermeneutic analysis on two sets of Cold War–era textbooks, one from the United States and another from Canada. We looked for important differences in how Cold War narratives are reproduced. Canadian textbooks were more likely to legitimize noncapitalist forms of organization, to allow for a more positive role for government, and to discuss communism and socialism more seriously and thoroughly. We argue that these differences are attributable to the divergent political context found in Canada during the Cold War era. The significance of this article is to recognize that discourses change geographically on a scale much smaller than anticipated.
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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.162 | 0.026 |
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".