Exploring Social Inclusion on Instagram: An Eye-Tracking Analysis of English and Italian University Language Centres’ Pages
Bibliographic record
Abstract
This study examines how university language centres (ULCs) use visual content on Instagram to convey the values of social inclusion. The analysis focuses on four ULCs affiliated with either the Association of University Language Centres in the UK and Ireland (AULC) or the Italian counterpart, Associazione Italiana Centri Linguistici Universitari (AICLU). Using a mixed-methods approach (eye-tracking data combined with thematic image coding), the study investigates: (1) whether and what images of inclusion are recognised by participants; (2) how recognisable and impactful they are for users similar to ULC audiences; (3) what type of conceptualisation of the specific values of inclusion are prototypically recognised by the group of participants. Findings reveal that while informants recognise inclusive imagery, it remains limited. Representations of ethnic, age, professional, and socio-economic diversity are more common than those of gender identity or diverse ability. Eye-tracking data indicate that the semantic traits conveyed by group images, particularly those showing intergenerational or professional variety, are more prototypical in informants’ conceptualisation of inclusivity. UK-based ULCs tend to use more visually impactful and inclusive content, while Italian centres present a less hybrid, more centre-specific and functional identity. The paper also reflects on the terminology used in inclusion discourse, proposing the use of a new blended term for disability: divability, which values diversity without reinforcing deficit-oriented language. This neologism, inspired by both linguistic economy and positive framing, is offered as a stimulus for further reflection. The study concludes with a discussion of the communicative and ethical implications of visual representation in language education, outlining directions for future research on user perception and inclusive design.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".