Diversification after resource crises; the case of tourism in the French archipelago of Saint-Pierre and Miquelon
Bibliographic record
Abstract
We use historical economic data of the Islands of St Pierre and Miquelon (SPM), a terri-tory of France situated 25 km south of Newfoundland (Canada), to explore different narratives of modern growth theory applied to small island economies when major changes in resource endowment occurs. The economy of SPM was less diversified than other territories because of privileged historical access to fisheries resources that un-derpinned the main sectors of the economy. That access was rapidly reduced because of changes brought about by the extension of the EEZ by Canada, and other fisheries management changes over time. The collapse of the northern cod stocks led to the cod fishing moratorium, imposed by Canada in 1992. This last major change, a crisis for SPM, provides the backdrop to explore the responses by the government of France and the population of SPM in the wake of the moratorium. Public investments were made in tourism to reorient SPM towards a more diversified economy. However, available data show an unresponsive tourist sector despite substantial amounts of public investment in infrastructure aimed at, among other things, improving tourism. These observations lend weight to various narratives of the eviction associated with public spending aimed for a long time at the fisheries sector, which made diversification difficult. The only sec-tor that seems to show evidence of diversification is the artisanal fishery. However, there is little evidence that this diversification was generalized to tourism despite im-portant public investment.\n
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| 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".