Resisting Colonialism: Indigenous Challenges to the Rhetorical History of a Canadian Conglomerate
Bibliographic record
Abstract
Suddaby et al. (2010) have persuasively argued that rhetorical history can be an important source of competitive advantage for firms. These authors hold that the stories that firms tell about their histories help them to achieve their objectives. This paper uses the experience of Canada's Hudson’s Bay Company (HBC), to refine our understanding of how corporate historical narratives are modified in response to pressure from social activists. Founded in 1670, the HBC is one of the oldest firms in the Western world. Since the start of the twentieth century, the organization has referred frequently to its history in its communication with consumers, workers, and other stakeholders. Since the 1960s, the HBC’s rhetorical history strategy has been adapted in response to profound changes in how Canadians remember their national past. The paper documents how the HBC responded to Indigenous social activists who contested the firm’s version of history by creating their own counter- narratives. As we show, the HBC preserved the credibility and thus usefulness of its historical narratives by changing them in response to shifts in the historical culture in which it was embedded. At the conclusion of the paper, the implications of our research for researchers and practitioners in diverse societies will be established.
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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 | 0.007 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.051 | 0.038 |
| Scholarly communication | 0.014 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".