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Record W2053136349 · doi:10.1177/1744935910387027

History as social memory assets: The example of Tim Hortons

2011· article· en· W2053136349 on OpenAlexaffabout
William Foster, Roy Suddaby, Alison Minkus, Elden Wiebe

Bibliographic record

VenueManagement & Organizational History · 2011
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicManagement and Organizational Studies
Canadian institutionsThe King's UniversityUniversity of Alberta
Fundersnot available
KeywordsNarrativeConstruct (python library)Argument (complex analysis)Competitive advantageSociologySocial memoryIdentity (music)Social constructionismStrategic managementEpistemologyManagementComputer scienceEconomicsSocial scienceCognitive scienceAestheticsPsychology

Abstract

fetched live from OpenAlex

Strategic management research has demonstrated that firm-specific resources can confer a distinct competitive advantage. This research, however, tends to assume that the resources are fixed and immutable and that they operate inside the organization. We offer a competing view in which resources are socially constructed and operate primarily on external stakeholders. Drawing from emerging research in social memory studies, we argue that historical narratives are an emerging means of socially constructing firm-specific social memory assets that can be used to create competitive advantage. We illustrate our argument through an analysis of how Tim Hortons, a now iconic Canadian company, uses historical and tradition-based narratives to construct its brand identity.

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 imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.843
Threshold uncertainty score0.313

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0130.023
Scholarly communication0.0060.007
Open science0.0010.003
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0040.000

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.

Opus teacher head0.036
GPT teacher head0.185
Teacher spread0.150 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations198
Published2011
Admission routes2
Has abstractyes

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