Increased Usage of Alt Text Is Required Across Ontario Public Library Social Media Feeds to Increase the Accessibility of Content
Bibliographic record
Abstract
A Review of: Hill, H., & Oswald, K. (2023). “May be a picture of a dog and a book”: The inaccessibility of public libraries’ social media feeds. Partnership, 18(1), 1–14. https://doi.org/10.21083/partnership.v18i1.7008 Objective – The research project sought to explore how accessible the social media feeds of Ontario public libraries are, particularly the use of alt text for images, by assessing the usage of alt text and by making recommendations for appropriate use within social media posts. Design – Collection of social media posts and computer-assisted textual analysis of visual media content. Setting – 76 public libraries and 9 public library systems in Ontario, Canada. Subjects – Approximately 900 Ontario public library social media posts from Facebook, Twitter, and Instagram. Methods – A random number generator sampling of 30 libraries per platform from the relevant social media accounts from a spreadsheet created using Ontario Public Library Statistics (OPLS) data of social media usage from the included libraries was initially created capturing 76 individual libraries. Then the researchers performed targeted sampling of posts from the nine library systems serving over 250,000 residents each. Researchers identified the 10 most recent posts from each included platform feed, and then undertook textual analysis for the presence of alt text with each post using two Mozilla Firefox browser extensions that determine the presence of alt text. Main Results – Of the 76 unique libraries chosen by the random sampling and the nine library systems that serve populations over 250,000, only two regularly used alt text and five had at least one instance of alt text. Only Toronto Public Library regularly included alt text across each of the three social media platforms analyzed by the study. The study also initially aimed to assess the quality of alt text used by public libraries in social media posts. However, due to the lack of alt text use across the sample, this was not possible at the scale initially aimed for, although a small number of examples are analyzed in the findings. Conclusion – The initial goal of analyzing the alt text to make recommendations for improved usage could not be realized due to the surprising lack of inclusion of any alt text across the sampled posts. This lack of any alt text can prevent some disabled users from engaging with content and information, leading to an inequitable experience. Public libraries should consider how accessible their engagement with users is and seek to improve the accessibility of social media posts.
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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 | 0.004 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.027 | 0.006 |
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".