Personality research in Lebanon: Personal reflections on challenges and opportunities
Bibliographic record
Abstract
In this paper, we seek to answer the question: “What is it like to do personality science in Lebanon?” Sharing our personal experiences, we reflect on the challenges and opportunities related to personality research in Lebanon, a Middle Eastern Arab country with a turbulent context. First, we discuss barriers to personality research that we have faced in Lebanon (e.g., scarce opportunities and technical resources to conduct research, the limited number of academics specialized in a particular research area, lack of national efforts to coordinate personality psychology research, limited funding opportunities, and economic and financial challenges, among others). We also present our personality research in Lebanon, particularly on trait emotional intelligence (trait EI), which has recently led to the implementation of Yes to Emotions in Youth, an impactful project funded by Grand Challenges Canada, aiming at developing trait EI and fostering mental health among vulnerable Lebanese youth in public schools. Finally, we offer recommendations based on our experiences, emphasizing areas for growth and opportunity despite the constraints.
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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 | 0.031 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.023 | 0.015 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.004 | 0.011 |
| Insufficient payload (model declined to judge) | 0.003 | 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".