Of Mice and Men: Educational Technology in Pakistan’s Public School System
Bibliographic record
Abstract
In this paper I use a critical lens to examine the introduction and adaptation of computer and information and communication technologies in Pakistan’s educational system. This examination is based on two broad contentions: a) the introduction of technology in Pakistan’s educational system is not conducive to the creation of a locally relevant knowledge system; instead the motivation is to create a market for foreign technology (hardware and software) and technological ideas; b) such an uncritical introduction of technology suits the needs of the undemocratic governments and hierarchical societies in the developing world and the neo-liberal economic forces abroad. I argue that such introduction of technology in education suits the former because, unlike critical education, the market model of education does not prepare students to question unjust and inequitable social and political practices around them. It rather suits the latter because education based on a market model produces a global pool of semi-trained laborers that can process technological and scientific raw material without gaining the expertise required to produce knowledge that is socially relevant and of benefit to them. I conclude that in this way technology becomes a source of hegemony and yet another tool of oppression rather than a vehicle for liberation and a just society.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.012 | 0.021 |
| Scholarly communication | 0.011 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.015 | 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".