The Fine Line between Person and Persona in the Spanish Reality Television Show La isla de las tentaciones: Audience Engagement on Instagram
Bibliographic record
Abstract
The hybridization of television genres has led to numerous non-fiction television shows that base much of their success on audience engagement through social networks. This study analyses a specific case, that of La isla de las tentaciones (Temptation Island), to identify interpretive frames in reality shows and their interrelationships with audience involvement on Instagram. Based on a corpus of 8409 comments posted on Instagram by the followers of the program’s actor profiles, the article analyzes the lines between reality and fiction in this non-fiction television show about relationships and infidelity, and, in particular, how online “haters” play a performative role. The show’s participants who were unfaithful are insulted and receive numerous negative value judgments. The “coding and counting” method, drawn from Computer Mediated Discourse Analysis, is used for the coding. Results show that viewers barely allude to this show as fiction, do not differentiate between the actors and their characters, and empathize strongly with the stories they view. The study shows the need for media education, both for those who make the media and those who view it. The goal is not to detract from entertainment value, but to improve critical skills and to recover the educational function of media.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".