MétaCan
Menu
Back to cohort

A Case Study of Technological Switching and Technological Lock‐In in the French Fisheries Sector: Why is Sustainable Change so Difficult?

2012· article· en· W2102917195 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
venuePublished in a venue whose home country is Canada.

Bibliographic record

VenueCanadian Journal of Agricultural Economics/Revue canadienne d agroeconomie · 2012
Typearticle
Languageen
FieldDecision Sciences
TopicInnovation Diffusion and Forecasting
Canadian institutionsUniversité du Québec à Rimouski
FundersFP7 Food, Agriculture and Fisheries, BiotechnologyEuropean Commission
KeywordsTrawlingTechnological changeEconomicsSubsidyBottom trawlingProximate and ultimate causationBusinessNonmarket forcesResource (disambiguation)FishingIndustrial organizationNatural resource economicsFisheryMarket economyPolitical scienceFactor market

Abstract

fetched live from OpenAlex

Many sectors such as the fishery show classic examples of technological lock‐in and path dependence, even though some economists might predict smooth switching toward technologies that are more cost effective and sustainable. We use ideas from the evolutionary economics and public choice literatures to explain why trajectories of technological change, especially in fisheries, may not be smooth at all, but rather punctuated. The interest of technological change and switching behavior for fisheries economists and managers stems from the fact that control of effective effort, often necessary for sustainable management of the resource, remains a central management problem for that sector worldwide, even in developed countries. However, various policies put in place by governments to support the fishing sector, and often put in place to “correct” for certain market failures, may inadvertently produce other “nonmarket” failures, which result in technological lock‐ins which are unsustainable. For example, the trawling technique was widely promoted in France in the 1970s and 1980s. Path dependency developed in such a way that the preferred choice of new entrants into the fishery was this technology. Technological lock‐in occurred on the trawling technique as the trawling sector also became more politically active, making it ultimately the most widely used technique in the French fisheries sector in the Atlantic. Switching away from this technology has not taken place even with poorer economic performance of that technology. This paper also discusses the influence of state subsidies on the adoption of trawling. Even if trawling was a major innovation in fisheries in the past, its potential for technological adaptations or minor innovations is limited now. These limitations are more obvious during periods of increasing energy prices, especially in the absence of state aid. However, due to collective choice phenomena, switches to more sustainable technologies will occur more slowly . Plusieurs secteurs, tel que celui des pêcheries, offrent des exemples de verrouillage technologique et de dépendances au sentier, alors même que les économistes s’attendent à un changement régulier vers une technologie plus efficace en termes de coûts et plus soutenable. Nous nous appuyons sur la littérature évolutionniste et des choix publics afin d’expliquer pourquoi les trajectoires du changement technologique, en particulier dans les pêcheries, peuvent ne pas être régulières, mais au contraire discontinues. L’intérêt des économistes et des gestionnaires pour le changement technologique et le comportement face au retour des techniques est lié au contrôle de l’effort réel, souvent nécessaire pour une gestion durable de la ressource. Le contrôle de l’effort reste le problème essentiel de la gestion des pêches à l’échelle mondiale, y compris dans les pays développés. Toutefois, de nombreuses mesures publiques mises en place par les gouvernements pour soutenir le secteur des pêcheries, afin de «contrecarrer» certaines défaillances du marché, peuvent déboucher involontairement sur d’autres défaillances «non marchandes», se traduisant par des verrouillages technologiques non‐soutenables. Par exemple, la technique du chalutage fut largement diffusé en France dans les années soixante‐dix et quatre‐vingt. Un sentier de dépendance s’est développé tel que les nouveaux entrants dans la pêcherie optaient pour cette technologie. Un verrouillage technologique s’est produit sur la technique du chalutage rendant ce secteur politiquement important, et finalement la technique la plus répandue parmi les pêcheries françaises de l’Atlantique. Un changement de technologie n’a donc pu survenir, même en présence de faibles performances économiques. L’article traite de l’influence des subventions étatiques liées à l’adoption du chalutage. Même si cette technologie fut une innovation majeure dans les pêcheries par le passé, les adaptations techniques potentielles ou les innovations mineures sont désormais limitées. Ces limites apparaissent clairement dans les périodes de coûts énergétiques croissants, notamment en l’absence d’aides étatiques. Toutefois, en raison du phénomène de comportements collectifs, les changements vers des technologies plus soutenables se déroulent avec lenteur .

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.768
Threshold uncertainty score0.920

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.115
GPT teacher head0.244
Teacher spread0.130 · 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