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
Recommender systems are all around us; they can be found in news applications, YouTube,\nNetflix, the healthcare industry, and e-commerce. These recommender systems are\ninfluencing our choices and the information that is presented to us. This makes it crucial to\nthink about the ethical consequences of these recommendations and possible solutions to\nethical issues. In this thesis, we have identified the main ethical challenges of recommender\nsystems, and we looked at one specific, promising solution called the secondary ethical layer.\nThe secondary ethical layer is a general ethical filter which filters out any unethical\nrecommendations based on cultural and personal preferences while also taking into account\nall the different stakeholders on which recommendations can have an effect (such as the user,\nprovider, system and society). We have found that this solution can solve some ethical issues,\nspecifically with regards to inappropriate content, unfairness (biases) and issues for society. It\ndoes not solve problems such as the lack of opacity and some privacy issues within\nrecommender systems. This thesis identifies different key elements of the ethical layer and\ncreates the fundaments on which a practical solution can be built.
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.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 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