Bibliographic record
Abstract
Technology offers great potential but can also create inequities and problems. One such inequity is the digital divide. The digital divide refers to the gap between those who can effectively use new information and communication tools, such as the Internet, and those who cannot. Those who are on the less fortunate side of the divide lose out in education, training, shopping, entertainment and communications opportunities. The causes of the digital divide are numerous, and include costs, access problems, lack of skills, cultural issues, and personal factors. As a result, reducing the digital divide needs to take a multi-faceted approach, which includes creating awareness and promotion, facilitating access, developing necessary skills, providing reliable support, developing suitable content, and ensuring community involvement. The mission of school libraries is threatened as long as the digital divide exists, and it is important that school libraries take steps to reduce this divide. These steps should include the approaches mentioned earlier, as well as using coordinated national, regional or local strategies, and collaborating with other organizations.
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 | 0.007 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.007 | 0.006 |
| Scholarly communication | 0.014 | 0.021 |
| Open science | 0.002 | 0.023 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.039 | 0.007 |
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".