Klipsun Magazine, 2006, Volume 37, Issue 02 - November
Bibliographic record
Abstract
In the face of opposition, it is often hard for a person to stay true to his or herself. We've all faced personal challenges that make us question who we are or what we're doing. For me, that challenge was believing in myself. When I first started taking journalism courses Spring quarter 2005, I wasn't sure if I was cut out for the major - the workload seemed too much. But I stayed with it, knowing that writing is one of my greatest skills and passions. Now, only a quarter away from graduation. I'm seeing more and more each day how being honest with myself and with who I am has paid off. In this issue of Klipsun, you'll meet others who have been just as honest with themselves and their values. In his interview with Justin Morrow, journalist and author Mark Fainaru-Wada exemplifies this value, despite potential criminal convictions. Local entertainer Betty Desire also shares her own story of staying true with writer Lauren Allain. I hope these stories help you understand your own motivtions, as well as those of others.
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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.228 | 0.129 |
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".