Bibliographic record
Abstract
De Bentonville, dans l’Arkansas, a la Chine interieure, Wal-Mart propose un cheminement assez classique du berceau de l’entreprise a une diffusion de ses magasins dans une quinzaine de pays. Mais a la difference des grandes entreprises transnationales comme ExxonMobil ou Microsoft, la premiere entreprise mondiale de distribution doit s’adapter a son environnement local pour attirer un maximum de consommateurs. L’article propose une approche multiscalaire des strategies de developpement de cette firme transnationale. Du local, nous retiendrons l’ideologie de depart du fondateur Sam Walton, a base de conservatisme, de discours chretiens sur l’ecoute de l’autre, le tout au service de la libre entreprise ; de l’echelle regionale et nationale, nous retiendrons aussi une preference pour les Etats du Mid-South, cette Amerique profonde, sans oublier les actions de lobbyings a Washington et l’emergence tardive d’une strategie d’expansion plus vaste pour preparer les futures implantations dans tous les Etats de l’Union. Quant a l’expansion a l’international, elle viendra tardivement, Wal-Mart se tournant d’abord vers un monde connu et securise (pour les investissements) comme le Mexique et le Canada, puis vers des pays susceptibles de comprendre le message et d’accueillir les entreprises nord-americaines. L’expression glocal n’a jamais si bien fonctionne que dans ce cas precis.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.023 | 0.003 |
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".