Bibliographic record
Abstract
The term open educational resources was initially coined at UNISCO's 2002 forum as an attempt to provide open access to teaching and research materials at no cost. Since that time, many initiatives have been proposed to support this goal but the cost of maintaining the digital material on the web remains a big challenge. Web content has traditionally been monetized by internet advertisements, which was one of the proposed solutions to cover the cost of maintaining educational resources. However, as the revenue-per-view and the click-through-rate for advertisements drop, content publishers are struggling to cover the operating cost required to maintain open-access service. To address this problem, we propose a solution that fills the niche that advertisements occupy while solving many of the problems associated with advertisements. This paper presents a new browser plugin that allows users to automatically donate a very small amount of Bitcoin to each website visited. The solution emulates the non-discriminate nature of advertising while providing a much higher revenue rate. It uses the in-place Bitcoin payment infrastructure to allow website owners to receive Bitcoin donations, and provides a simple interface that offers website visitors full control over the amount they want to donate to support each educational resource.
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 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".