The Tunisian Economics’ Situations after the Revolution of Arab Spring 2011
Bibliographic record
Abstract
The Tunisian economic facts after the so-called the Arab spring or social revolution have been marketed by numerous fluctuations and radical changes in the general situation of the management of the administrative affairs of the country. The most prominent of these facts, including the series of chaotic sit-ins and the political and security instability that has increased from 2011 to 2018, note in particular the emergence of the phenomenon of terrorism and assassinations. These negative results are too the expensive cost of the Tunisian national economy, which has been directed, affected by all vital sectors of the country’s economy, especially the tourism, trade and investment sectors. In addition, the increase in excessive wages during the first three years following the revolution and the increasing number of random sit-ins that led to the cessation of the production in the Gafsa phosphate mine and the failure to work for most of the public servants represented negative factors that led to a decline in productivity and an increase in the financial and trade deficit. Thus, the budget deficit and the accumulation of indebtedness represent the main obstacle to achieving social and economic stability.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".