Determining the Status and Use of Languages Spoken in Pakistan
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
Developing economies in South and South East Asia are faced with numerous challenges socioeconomically, politically and culturally. The multilingual and multi-ethnic makeup of these societies including Pakistan shows a marked confusion to come to terms with a uniform language policy. At the root of this confusion there is, on the one hand, a growing ascendency of English globally and, on the other hand, the downward trend or at least stagnation in local languages for the failure of these states to have comprehensive strategies to render them vital. Language planning and determining the value of local languages in Pakistan has always been a point of debate in the political, legal and constitutional history of the country. In the pre-partition era, the sub-continental history was marked by Urdu-Hindi controversy, while after 1947, the latter was replaced by Bengali that remained a great source of unrest and ultimately proved an impetus in the division of Pakistan and Bangladesh into two separate countries (Mustafa, 2011). While the country is still grappling with the issue, it is assumed that revisiting its own past policies and the conscious efforts of Malaysia, Switzerland, Nigeria and Canada shall serve as a roadmap and shall inform the stakeholders to avoid time-tested mistakes. This study, thus, takes into consideration the history of language planning in Pakistan and presents cases of other countries that have already embarked on such policies to varying degree of successes. DOI: http://dx.doi.org/10.7220/2335-2027.4.3
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it