Bibliographic record
Abstract
As a result of the COVID-19 pandemic beginning in the Spring of 2020, vulnerable Canadians were left behind by digital exclusion, which was exacerbated by an increased reliance on digital technologies. In this article, I seek to provide an overview of the links between digital inclusion, social justice, and the values of the LIS profession. Because of the COVID-19 pandemic crisis, another crisis of digital exclusion has revealed the ways in which digital citizenship and socio-economic exclusion are fundamentally intertwined. In response, many LIS professionals have overcome extensive closures and reductions in resources to find innovative solutions to this crisis of inequality. This article will provide just a few examples of these responses from LIS organizations. Indeed, even among overwhelming barriers, LIS professionals have not lost sight of community values and commitment to social justice in challenging times. In unprecedented times, LIS professionals have found innovation to address ongoing social and economic barriers of digital exclusion.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".