Like, comment, and share the university experience: exploring the narratives present in Ontario universities' Instagram profiles
Bibliographic record
Abstract
<div>The main objective of this study was to explore the visual content and discourse present in three Ontario universities’ Instagram profiles, particularly their profiles dedicated to student recruitment and admissions. The study looked at the Instagram content published by top-three high application volume universities: McMaster University (macadmit), Ryerson University (whyryerson), and University of Toronto (futureuoft). The data collected was composed of photos, videos, and captions from all three universities from September 1, 2020 to December 31, 2020 inclusive. The results showed that visual assets highlighting the institution, portraits of students, and event/advertisements were the most occurring types of Instagram content. In addition, there was a high use of event/advertisement style in the accompanying captions. When the visual assets and captions were juxtaposed against each other, there was a high incongruence between the pairing which can be laborious for readers who will need to consolidate the visual and text information.</div>
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 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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".