Bibliographic record
Abstract
In the never-ending quest to tell the University of Alberta’s story to as wide an audience as possible and further enhance its reputation, the current authors developed and created a brand journalism site, folio.ca, that is independent yet connected to the University of Alberta. The objectives were simple: increase the number of people who read stories about University of Alberta and obtain increased media coverage from legacy and new media outlets. This paper explains the rationale for the move in what is an everchanging media landscape in which consumers get their news from an ever-increasing number of media outlets and discusses the steps taken to develop the website and obtain buy-in from all corners of the academy. The paper provides details of the implementation plan, which included four foundational pillars — inverted pyramid structure to tell our news stories, change management plan, designing the news site and developing an editorial mandate. The paper describes how the results far exceeded the current authors’ expectations. Every key performance indicator identified — including most notably, page views and media mentions — grew dramatically. In Folio’s first year, page views increased 86.6 per cent while external media mentions rose by 24.5 per cent. Furthermore, insights gained from the Folio audience, compared to the audience who visit the university’s institutional website, suggested a broadening of the audience who read stories about the University of Alberta. The results demonstrate that if a university builds a communication tool that uses the traditional techniques of journalism to tell stories that draw on the expertise of the institution and is carefully targeted to an external audience, it can develop an audience and increase the pickup of its stories by external media outlets, resulting in a broadening of its ability to tell its story to the public.
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 | 0.005 | 0.001 |
| 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".