Experiences With Building A Narrative Web Content Management System: Best Practices For Developing Specialized Content Management Systems (And Lessons Learned For The Classroom)
Bibliographic record
Abstract
In this chapter, I begin by examining the process of creating a specialized online content management system (CMS) and conclude by applying the techniques and lessons learned from this experience to classroom pedagogy. Specifically, I consider the development of a Web-based CMS that was created using stories as the raw material for propagating organizational knowledge (a more detailed description of this process is found in McDaniel, 2004). While the theoretical basis for such an effort is an interesting study in its own regard (see Denning, 2001; Post, 2002; Smart, 1999 for studies of storytelling at work in organizations such as the Bank of Canada, the World Bank, and NASA; or Kim (2005) for a discussion of narrative as it applies to the field of technical communication), the issues involved with the construction of such an interface deserve their own unique discussion. In addition, this humanities-friendly data model presents an opportunity for studying the implications of using content-compatible CMS design methodologies in a classroom with advanced writing, communications, or digital media students.
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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 | 0.016 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.008 | 0.011 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".