Bibliographic record
Abstract
All in all, I feel incredibly grateful that I was able to attend the ACU PR, Marketing & Communications conference and learn from international industry leaders and peers alike. It was a fantastic networking opportunity and the marketing/recruitment knowledge gained was invaluable. I feel confident that I will be able to apply this knowledge to engage prospective students and create awareness of AU to the general public. One of the biggest highlights at the conference was receiving the award for best “Student Publications” at the award ceremony and gala. There were only four categories (Student Publications, Corporate Publications, Websites, Outreach & Community Relations) and only one winner in each category. Hundreds of entries were received from all over the world and an international panel of experts assessed each submission. I submitted AU’s recruitment publications (AU Viewbook and Book of Answers) in the Student Publications category and was very proud to represent AU as the only Canadian university to receive an award While the conference focused on marketing practices in higher education, its value will be applied to other areas of Advancement. This includes workshops that focused on the impact of reduced budgets, demands from staff and students for greater engagement and improved communication, and the growing importance of marketing and communications in strategic planning at an institutional level - all of which are beneficial to my colleagues within Advancement. If there is any critique of the conference it is only that I wish there had been even more opportunities for networking and engaging in discussion outside of the presentations and workshops. The conversations I did have were so spirited and engaging that I only wish I had more of them. Overall, the ACU PR, Marketing & Communications conference was an excellent and informative professional development experience.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.519 | 0.245 |
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".