Bibliographic record
Abstract
What's coming for career resources at CFA Institute in 2016?It's definitely going to be an exciting year.One of our biggest projects is to relaunch our CFA JobLine service.That's an exclusive job board that we offer for CFA Institute members, where we work with employers to post the top investment management jobs globally.It's served our members well in the United States and Canada, but we've seen less traction in other regions.We want to make it a truly global service.We are currently in the vendor selection phase, and we anticipate a full launch around September.A key goal of this project is to serve our Asia-Pacific and European members a lot better than we do now, and to expand the job opportunities in those regions.Our strategic imperatives for the project are twofold.First, we want to leverage our CFA JobLine service to grow and strengthen employer awareness of an affiliation with CFA Institute and thereby increase the demand for our members' qualifications.Second, we provide a platform for our member societies to strengthen their offerings to members and benefit from their local context.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.008 | 0.003 |
| Scholarly communication | 0.017 | 0.012 |
| Open science | 0.001 | 0.013 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.130 | 0.073 |
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".