Ethics and challenges of databased decision making processes in educational contexts
Bibliographic record
Abstract
Heidegger proposed in 1977 that the world was in transition from the modern to the technological way-of-being, in which humans and objects act upon one another in ways that mutually transform their characteristics. At that time technologies were simple and could be classified as tools. Emerging technologies are different. They are complicated assemblages of data and artefacts, and their availability to humans involves models made of multiple algorithms produced by computer scientists. This complicates scrutiny of the tools and assessment of their value to augment the human/technological way-of-being and learning. However, the models can bring interesting developments to the education field for the personalization and filtering of large amounts of data and can help in the management of learning. The challenge is that when analysing the available tools and strategies for filtering and managing the information stream it becomes clear that software and algorithms are not simply lines of code, but that they are shaped by social, political and economic interests that influence their value for learning. The purpose of our research was to critically analyse the ethics of the new developments. Our research shows that the validity of some models proposed does not conform to reality and predictive accuracy, but rather on the usefulness of the technological models proposed. Our research includes concrete examples to highlight the challenges with this approach.
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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 | 0.096 | 0.133 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.008 | 0.065 |
| Scholarly communication | 0.021 | 0.013 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.008 | 0.008 |
| 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".