Fintechs – grandes institutions financières : comment faciliter le succès de la collaboration pour nourrir l’innovation
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.
Bibliographic record
Abstract
La dynamique de collaboration entre petites start-up du domaine de la finance technologique, communément appelées « fintechs », et grandes institutions financières (GIF) traditionnelles, telles que les banques et les sociétés d’assurances, intrigue. Initialement considérées comme entreprises concurrentes, les fintechs et les institutions financières se retrouvent face à la nécessité de collaborer, en vue d’accélérer l’innovation de technologies financières. En s’appuyant sur trois études de cas de collaborations actuelles entre fintechs et institutions financières au Canada, cet article analyse les facteurs qui leur permettent de mieux collaborer et entretenir leur relation. Dans ce cadre, deux constats sont mis en avant : 1 – la dynamique de la relation se base surtout sur une logique de coopération et non pas de compétition ; 2 – il existe plusieurs mécanismes importants (individuel, structurel, et de processus), sans lesquels la coopération ne peut se développer. Pour finir, cet article offre aux praticiens des recommandations pour réussir leur collaboration.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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 it