Un recorrido pandémico de las prácticas docentes mediadas por la tecnología desde la perspectiva del determinismo tecnológico
Bibliographic record
Abstract
Technological determinism was born in the Chicago School with figures like William Ogburn, and shows us its most radical version in Jacques Ellul. But it will be Marshall McLuhan, promoter of the Toronto School, who transcends this approach, since technology goes from being the determining factor per se to the determining factor in social progress and planetary globalisation. The essence of these ideas lies in the autonomous capacity of technology, becoming the “determining factor”.During the pandemic we have and are once again witnesses to this fact, and it is the analysis of it that is the purpose of this essay. Communication has been the subject of a redefinition on a global scale that highlights the inescapable presence of determinism in our days; so much so, that he unstoppably enters the classroom. Are technological determinism and teaching compatible? Can the former be a tool for the latter? We deal with that in the following lines.Under these deterministic approaches and postulates, which are part of the traditions of communication studies, we propose to analyse as an essay the role that the media have played during the COVID-19 pandemic. Undoubtedly, this necessary revisionism and tension of classical theories with current contexts, loaded with uncertainties and relativisms, constitute a contribution to the necessary discussions that must take place.
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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 | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.021 |
| Scholarly communication | 0.016 | 0.008 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 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".