A day at the university fair: ‘hot’ brands, ‘house of brands’ and promotional tactics in higher education
Bibliographic record
Abstract
Research on promotional behavior within higher education has exploded over the last two decades, spurred on by the intensification of student recruitment. To date, studies have focused on mapping the content of conventional promotional texts (e.g. viewbooks, web sites), to identify how institutions depict themselves through them. By comparison, recruitment events, such as exhibitions or fairs, have received limited scholarly attention. This study aims to ameliorate this gap within the present literature, using observational methods and collaborative auto-ethnography to analyze branding strategies and broader social dynamics within a prominent Canadian university fair. Using such methods, this study identifies (i) variance in the uptake of ‘house of brands’ and ‘branded house’ strategies, (ii) diverging degrees of student interest across institutional types, along with (iii) ‘niche’-oriented marketing tactics across information booths. Observed patterns are theorized from the standpoint of contemporary research within the field of organizational sociology and higher education marketing.
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 | 0.001 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".