Bibliographic record
Abstract
Critical theory designates a philosophy and a research methodology that focuses on the interrelated issues of technology, politics and social change. Despite its emphasis on technology, critical theory arguably remains underutilized in areas of practical research that lie at the confluence of social, political and technological concerns, such as the study of the use of the usability of information and communication technologies (ICTs) or of their use in educational institutions. This paper addresses this situation by first describing the methodology of ideology critique. This critical methodology operates comparatively, by "measuring" consensual truths against actual social conditions. In doing so, it frequently shows these truisms to have the quality of mystifications or "myths," claims possessing a "false clarity" and that are misleading in developing and justifying research plans and priorities. Focusing on the specific example of e-learning (or the use of ICTs in education), this paper shows how critical theory can be used to "de-mystify" three particular truths or myths. These are claims that 1) we live in a "knowledge economy," 2) that users enjoy ubiquitous, "anywhere anytime" access, and 3) that social and institutional change is motivated by a number of fixed "laws" of progress in computer technology. These claims are shown to simplify or obscure a complex social reality that is constituted by different and conflicting forms of knowledge, and these claims are shown to work to the benefit of interests that are hegemonic and conservative in nature.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.019 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.007 | 0.039 |
| Scholarly communication | 0.015 | 0.012 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.020 | 0.005 |
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".