Measuring literacy: A scoping review protocol
Bibliographic record
Abstract
The charitable non-profit organization Literacy Nova Scotia (LNS) seeks to understand the state of literacy in the province. This necessitates reconceptualizing traditional understandings of literacy by accounting for emerging literacy types and considering literacy as practices rather than skills. A vast body of literature on literacy exists that seeks to measure and assess different types of literacy and their current state in various contexts. The objective of this scoping review is to survey these efforts and answer the overarching research question: What different forms of literacy exist and how have they been defined and measured in the literature? Peer reviewed and grey literature will be identified; our search will be limited to the English language, and literature published after the year 2014 for currency. It will consider peer-reviewed journal articles in Canada, but grey literature produced across the globe. This study will identify, appraise, and chart relevant existing literature. Key sources to be searched include the large bibliographic database Scopus and subject-specific databases in the field of Library and Information Science and Education. Grey literature will be retrieved using Google, by hand searching literature produced by adult education and literacy organizations, and through consultation with experts. A first round of screening will be conducted to identify literature that meets inclusion/exclusion criteria. A categorized list of different types of literacies identified from the first round of review will be produced and LNS will select types most relevant to their organizational objectives and focus. A second round of screening will take place based on these updated criteria. Data will be charted according to our data extraction form, and results will be collated and summarized. Bibliometric methods will be used to produce aggregated insights from the initial corpus of literature retrieved through our search strategy pre-screening. The authors kindly request any feedback on this protocol be provided to corresponding authors by email by February 15th, 2024.
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 | 0.139 | 0.112 |
| Meta-epidemiology (narrow) | 0.005 | 0.007 |
| Meta-epidemiology (broad) | 0.011 | 0.011 |
| Bibliometrics | 0.021 | 0.017 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.010 | 0.011 |
| Open science | 0.007 | 0.008 |
| Research integrity | 0.009 | 0.009 |
| Insufficient payload (model declined to judge) | 0.139 | 0.033 |
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".