Bibliographic record
Abstract
ii ACKNOWLDEGEMENTS iii TABLE OF CONTENTS iv LIST OF TABLES......... ix LIST OF FIGURES x CHAPTER 1INTRODUCTION 1 Research Questions 2 Autobiographical Signature 2 The Goal of Scientific Literacy 5 Rationale 9 A Current Gap in Scholarship 10 Overview of this Study 11 CHAPTER 2 LITERATURE REVIEW 13 Part 1: Scientific Literacy 13 Literacy: A Label with many Applications 14 Scientific Literacy: A Label with Multiple Interpretations 16 Background and Current State of Scientific Literacy Initiatives 17 Theoretical Perspectives on Scientific Literacy 23 Pedagogy for Scientific Literacy: A Strategy, not a Slogan 25 A New Framework: the Scientific Literacy Framework 27 The Need for a New Approach 46 Summary of the Scientific Literacy Framework 48 Part 2: Applications of ICT for Science Education 53 A Focus on Scientific Literacy Framework Compatibility Other Technology Initiatives 56 Wiki Projects 57 Social Networking Sites 62 Section Summary 69 Effectively Linking Communication Technology with Scientific Literacy 69 Other International Initiatives 70
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 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.066 | 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".