Bibliographic record
Abstract
This report provides an introduction to online polling in its various forms (questionnaires, quizzes, surveys, assessment products, etc.), and discusses its advantages and problems in online education. What is Online Polling?The advent of online technologies during the 1990s has led to the development of numerous new automated data collection techniques and pre-configured Web polls (Ostendorf, 1994).These tend to emulate hand-held keypad systems used for anonymous polling in political and advertising research (Baggaley, 1997).Uses of the term "poll" differ widely.Mancinelle (2003) suggests that polls refer to a single question, while surveys are more complex.An earlier report in the current series (click here for Technical Report XII) however, has recommended the use of the term "online polling" in referring generally to "questionnaires, quizzing, survey and assessment products" (Baggaley, Kane, and Wade, 2002).The online format typically associated with these activities, is one in which participants place closed-ended "votes" in response to fixed questions or statements, and in which the votes are counted.The current use of "polling" as a generic term is thus consistent with the definition of "polling" provided by The Oxford Dictionary (Sykes, 1976), as being associated with voting and mediated by the counting of ballots.For the purposes of the current discussion, an online polling system may be further defined as an asynchronous or real-time process of information gathering, obtained via responses to question(s) mediated by Web-based formats.N.B.Owing to the speed with which Web addresses are changed, the online references cited in this report may be outdated.They can be checked at the Athabasca University software evaluation site: http
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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 | 0.009 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.017 | 0.006 |
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".