Performing Pan American Airways through coloniality: an ANTi-History approach to narratives and business history
Bibliographic record
Abstract
This paper centers on the role of narratives in business history from an ANTi-History perspective. We focus on the networked processes through which narratives are told of, for, and by multi-national companies embed the development of ‘new imperialism’ and coloniality. We set out to achieve this through a discussion and application of ANTi-History to a study of Pan American Airways and particularly its performance as a maturing multi-national company and its relationship to postcoloniality. In the process, we also hope to contribute to recent calls in business history for more explicit accounts of the methods used in the development of historical accounts. We are concerned to encourage ‘a narrative turn in business history’ but also to do so critically, i.e. to reveal some of the strengths and limitations of a narrative turn. We conclude with reflections on the relationship between methods and the production of (business) history.
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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 | 0.010 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.015 | 0.049 |
| Scholarly communication | 0.014 | 0.015 |
| Open science | 0.001 | 0.007 |
| 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".