Instagram como canal de comunicación en el ámbito académico. Comparativa de las estrategias de las mejores universidades del mundo
Bibliographic record
Abstract
Universities use social networks to transmit their institutional identities, applying them as mirrors and loudspeakers of campus life. Thus, they intend to attract potential students and build global communities that transcend the offline field. Much of the previous studies give greater relevance to the engagement achieved than to the discourse used, so this research explores the use of Instagram by the five best universities in the world according to the Shanghai 2022 Ranking, with the aim of comparing their strategies and contrast published content with recorded interactions. The methodology focuses on a quantitative and qualitative content analysis of the posts published by Harvard, Stanford, MIT, Cambridge and California Berkeley during the first quarter of the 2021/2022 academic year (n=394), for which a file of analysis has been designed.\nThe results reflect a certain homogeneity in terms of formats, with a predominant use of images; and to discursive intention, focused on extolling the human capital of the institution and its life stories. However, there are particularities derived from the values of each University and the idiosyncrasy of the territory in which they operate. Likewise, from the relationship between the semiotics of the message and the registered interactions, a main conclusion is drawn: there is a discrepancy between the most published and the content with the most participation. Thus, to achieve bidirectionality in their social community, institutions should design their strategies according to the impact achieved, which is greater when CSR actions are disseminated and video is chosen.
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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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.013 | 0.006 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".