Are There Inclusive, Accessible Reference Tools for the Post-Pandemic Era?
Bibliographic record
Abstract
Attempts to integrate the twain (i.e., social justice [SJ] and civic engagement [CE]) are slowly emerging. This chapter critically explores the tools for inclusivity and engagement -- to facilitate developing digital literacies for an integrated program. Among the roles of LAM, such as, literacy, collaboration, outreach, advocacy, etc. this chapter deals with digital literacies -- the aim is to reduce the digital divide between haves and have-nots. The digital divide became most obvious during COVID-19, and therefore this dimension is the focus. The method adopted is a semi-automated strategy to support a rationale for analysis and validation of its findings. Strongly recommends the need to conduct COVID-19's impacted digital exclusion areas -- with due consideration for the work done at New Literacies Research Lab at the University of Connecticut. A combined quantitative and qualitative assessment will be required to remove the digital inequalities.. An innovative approach for data visualization is provided. It is a faceted technique developed by Dr. S R Ranganathan (viz., PMEST – personality, matter, energy, space, and time).
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.502 |
| Open science | 0.001 | 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".