Una aproximación cualitativa a la RSC en la banca cooperativa
Bibliographic record
Abstract
Every day more enterprises are aware of the importance of corporate social responsibility CSR for their business short and long term success. They display an awareness of it’s need to be part of the corporate strategy and be shown as a priority and as part of the company’s culture. For more than a century, cooperative banking has been capable of showing its strength and utility, preserving inherent elements that bring it closer to certain CSR principles, in a key sector, which makes it a great ally with powerful changing leverage in terms of social responsibility. However, even though there is substantial research related to CSR, there is scarce information on its relation to cooperative banking. For the aforementioned, I propose to analyze in depth the situation of CSR in cooperative banking as the general aim of this doctoral thesis, so as to move forward in this field in theoretical terms through a rigorous qualitative study. The main goal of this thesis can be outlined through a number of specific objectives as follows: 1) find the theoretical implications and connections of CSR with cooperative banking; 2) detect if there are diverse management models of CSR within cooperative banking; 3) find the main differences among these models, studying their weaknesses and strengths, both at individual and generalized levels, in the management of CSR; 4) finally, propose improvements that, along with the observed and highlighted strengths, serve to suggest an ideal management model of CSR associated with cooperative banking. To conduct these objectives, case study qualitative methodology was applied. This methodology was implemented in three selected case studies (Grupo Cooperativo Cajamar, Rabobank and Desjardins) that represent cooperative banking models in their home countries (Spain, Netherlands and Canada). The structure of this doctoral thesis consists of a theoretical framework, in which CSR, cooperative banking and their relation will be extensively analyzed. Then, an empirical framework starting with a chapter in which the methodology used in this thesis is explained in depth, a chapter for each case study, and concluding with a chapter comparing case studies. The conclusions are presented in the final chapter of this thesis, underlining the achieved objectives, specifying the principal limitations of the study and proposing other research lines to address in the future.
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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 | 0.010 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.009 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.008 | 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 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".