Bibliographic record
Abstract
html?res=F70710F8395C0C778EDDAE0894DA 404482. Association for Education in Journalism and Mass Communication (AEJMC). (2005, June). World journalism education congress. AEJMC New,. 38(5), 1. Campbell, Cole C. (2005, August 12). Strengthening our place: The role of journalism programs on university campuses (round-table discussion). Association for Education in Journalism and Mass Conference, annual conference, San Antonio, Tex. International Communication Association (ICA). (2004). Petition. URL: http://www. icahdq.org/divisions JournalismStudies/jsigweb4/ICA_JSIG_petition.pdf. Kovach, Bill, & Rosenstiel, Tom. (2001). The elements of journalism: What newspeople should know and the public should expect. New York, NY: Three Rivers Press. Johansen, Peter, & Dornan, Christopher. (2003). Journalism education in Canada. In R. Frolich & C. Holtz-Bacha (Eds.), Journalism education in Europe and North America (pp. 65-90). Cresskill, NJ: Hampton Press. Mangan, Katherine S. (2005, June 3). Plan would change journalism education. The Chronicle of Higher Education. URL: http://chronicle.com/weekly/v51/i39/39a00802.htm. Medsger, Betty. (1996). Winds of change: Challenges confronting journalism education. Arlington, VA: The Freedom Forum. Medsger, Betty. (2003). Reconsidering those little questions: Who? What? When? Why? How? Conference on Journalism Education. November 8, 2003. Ryerson Univer-
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 | 0.005 | 0.031 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.015 | 0.008 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.008 | 0.003 |
| Research integrity | 0.050 | 0.048 |
| Insufficient payload (model declined to judge) | 0.024 | 0.011 |
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".