Bibliographic record
Abstract
This paper examines how to pedagogically approach the issue of information search and usage in conventional and generative artificial intelligence-based search engines, conversational agents (chatbots), and social media platforms. Search engines are con sidered insofar as they require students to clearly define a complex problem to be re searched – with the aim of carrying out a school or academic work using advanced search techniques; these techniques are then compared to those of searching by means of traditional online information services using Boolean operators and search filters. Search engine optimization (SEO) is discussed as a method for choosing and strategically allocating keywords on websites that encourages the use of the advanced search techniques. Conversational agents are examined, which require students to develop skills like those needed for advanced search in search engines, aiming at con structing effective prompts (detailed instructions) in relation to the goals of the query. The role of social networks is examined and its modus operandi in digital environments characterized by algorithmic action as well as the necessity of developing critical awareness regarding the exposure of personal information, interface configuration, and search results. Projects and initiatives of pedagogical actions from other countries (United States, France, and Canada) are discussed with the aim of inspiring didactic action on these themes, bringing together principles of digital technology as well as media and information literacy (MIL)
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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 | 0.007 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.015 |
| Scholarly communication | 0.010 | 0.019 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.008 | 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".