Learning to use the past: the development of a rhetorical history strategy by the London headquarters of the Hudson’s Bay Company
Bibliographic record
Abstract
Organization studies scholars are increasingly interested in how managers use the past to obtain competitive advantage. Little research has been done on the history of the corporate use of history which means that we know little about the circumstances in which the corporate use of rhetorical history was pioneered. This paper historicizes rhetorical history. It uses the experience of the Hudson’s Bay Company (HBC) to develop understanding of how companies used the past to advance interests in the face of political threats. Founded in 1670, the HBC is one of the oldest firms in the Western world. For much of its history, its senior managers invested few resources in the firm’s ‘heritage infrastructure’ and rarely used history in its communication with outside stakeholders. This paper shows how it learned to use history as a strategic asset gradually and by observing other firms. At the end of World War I, it began to make substantial investments in heritage infrastructure. This allowed the firm to turn its long history into an asset. This paper stresses the politicized nature of the corporate use of the past.
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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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.010 | 0.016 |
| Scholarly communication | 0.013 | 0.008 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".