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 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 | 0.009 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.004 | 0.008 |
| Scholarly communication | 0.021 | 0.046 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.033 | 0.017 |
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".