Bibliographic record
Abstract
An exciting new consumer advertising campaign to launch this fallWhat led to the open Your eYes campaign?Earlier this year, the team from Ogilvy conducted interviews with CAO and NPEC committee members from coast to coast.Based on the feedback that they received, it was clear that we needed to generate more impact and to assert the role of optometrists:• There is growing concern that other provinces may follow British Columbia in deregulation of dispensing glasses and contact lenses.• The differences between optometrists, opticians and ophthalmologists are not obvious to patients.• The general public remains unaware about the need for regular and ongoing management of eye health.Even with 20/20 vision, 1 in 7 Canadians will develop a serious eye disorder.See an optometrist for a complete exam.opto.ca/openyour eyes Open yOur eyes On Monday September 26 th a new CAO advertising campaign will be launched across Canada to kick off Eye Health Month.Television ads will run on the majority of major networks in English and French markets, including CBC, CTV, Global and Radio-Canada, as well as many specialty cable channels that are popular with our target audience.The campaign is called Open Your Eyes and will run at high media weights until the end of October.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.230 | 0.071 |
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".