Evolution of the Lebanese banking sector legitimacy in view of the pressure of the United States Regulations
Bibliographic record
Abstract
In the recent years, many international banks have been fined because of violations of US regulations. Also, their legitimacy was challenged, and questions were raised about the strategic responses adopted by these banks to restore their legitimacy. In Lebanon, the collapse of the Lebanese Canadian Bank (LCB) due to non-compliance with US regulations has shaken the whole banking industry whose legitimacy has been affected, in addition to the compliance measures taken by this sector in face of the mounting pressure of the US Legitimacy, a major concept of the neo institutional theory is crucial for the survival of organizations. This article postulates that the Lebanese banking sector is on continuous quest for the legitimacy of the US regulator on which it is dependent to survive. Thus, the aim of this paper is to explore how the Lebanese banking sector’s legitimacy has evolved over time in view of the pressure of the US, by addressing the following question: How the US regulations affect the perception of the Lebanese banking sector legitimacy? Towards achieving this objective, this paper utilizes a retrospective longitudinal design and undertakes a qualitative content analysis of archival data from 1997 to 2018. Our findings revealed that legitimacy in the Lebanese context has passed from a cognitive legitimacy conferred by a domestic regulator before LCB to a more “pragmatic” one that is conferred by a foreign regulator after LCB.
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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 | 0.007 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 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".