How a FIRM (Flexibility, Innovation, Robustness, and Maturity)Argument for FOSS (Free and Open Source Software) Can \nDisplace FUD (Fear, Uncertainty, and Doubt)
Bibliographic record
Abstract
This paper discusses how a coalition of Athabasca University (AU) faculty successfully promoted Moodle, an open source learning management system (LMS), as a viable alternative to two major proprietary LMSs: WebCT Vista and Lotus Notes.The evaluation tool a group of core users developed to determine AU's choice of LMS is described and the evaluation results are touched upon.The evaluation process, however, was not a neat, technical exercise, but rather a process of debate, contention, disagreement, and compromise.Because an LMS resides at the confluence of the social and technological, choosing an LMS is not a purely technological act, but rather a communicative process that can be fraught with political, economic, and cultural factors, as well as personas.Advocates of open source software need to remain fully cognizant of this fact and be prepared to calmly provide evidence of flexibility, innovation, robustness, and maturity (FIRM) whenever institutional and/or personal objections appeal to and/or promulgate arguments against open source software based on fear, uncertainty, and doubt (FUD).Athabasca University (AU) faculty first learned of the decision to adopt WebCT Vista as the University's Learning Management System (LMS) in a Universitywide email circulated by Athabasca's Chief Information officer (CIO) on Wednesday, September 17, 2004:In order that we can provide stable, sustainable and world class courses and learning experiences for students, I have recommended, after extensive research and review, that AU adopt WebCT Vista as its learning management system (LMS).I have further recommended that we migrate to this platform through a transition process with an objective of completion by the end of 2006.This seemingly arbitrary decision deeply troubled AU's faculty for a number of reasons; the most significant, however, were: a lack of consultation; the declaration that choosing an LMS was a non-academic matter; and the absence of factual evidence to support the choice of WebCT Vista.
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 | 0.031 | 0.066 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.008 | 0.038 |
| Scholarly communication | 0.020 | 0.017 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.010 | 0.010 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".