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Who Are the Educators and How Can We Help Them?

2016· book-chapter· en· W3103587581 on OpenAlexaboutno aff
Arthur Lupia

Bibliographic record

VenueOxford University Press eBooks · 2016
Typebook-chapter
Languageen
FieldSocial Sciences
TopicEducator Training and Historical Pedagogy
Canadian institutionsnot available
Fundersnot available
KeywordsIgnorancePoliticsPolitical scienceConstitutionPublic relationsCompetence (human resources)LawPsychologySocial psychology

Abstract

fetched live from OpenAlex

From this point of the book forward, I ask you to think about the challenges described in chapter 1 from a civic educator’s perspec­tive. That is, consider the perspective of a person who wants to increase other people’s knowledge about politics or their competence at a politically relevant task. With that perspective in mind, I first convey a few important facts about civic educators and their aspirations. Next, I present a plan for helping many of them achieve these aspirations more effectively. The plan covers the book’s main themes and offers a chapter-by-chapter description of what’s ahead. Let’s start with a brief discussion of “ignorance”—a topic that motivates many people to become civic educators. According to numerous surveys and news reports, the mass public appears to know very little about politics, government, and policy. When pollsters ask even simple questions on any of these topics, many people fail to give correct answers. For example, while nearly every adult American can name the president of the United States, many cannot recall the names of their US senators. Millions cannot easily remember which political party holds a majority of seats in the US House of Representatives or the US Senate. Many Americans give incorrect answers when asked to identify the chief justice of the United States by name. They do the same when asked about basic aspects of the US Constitution. Many provide incorrect answers to questions about who leads our nation’s closest international allies, such as the United Kingdom and Israel. Most seem not to know basic facts about our major trading partners, such as Canada and China. People provide incorrect answers or no answers at all to survey questions about all kinds of policies and politics. In “The Star Spangled Banner,” America is “the land of the free and the home of the brave,” but when asked to answer fact-based questions about policy and politics, Americans appear to be, as filmmaker Michael Moore (2010) put it, “a society of ignorant and illiterate people.”

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 imitation

Not 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.

metaresearch head score (Codex)0.010
metaresearch head score (Gemma)0.022
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.026
Threshold uncertainty score0.082

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0100.022
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0110.009
Scholarly communication0.0260.031
Open science0.0020.008
Research integrity0.0110.025
Insufficient payload (model declined to judge)0.0250.022

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.

Opus teacher head0.082
GPT teacher head0.268
Teacher spread0.186 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreOther

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".

Quick stats

Citations5
Published2016
Admission routes1
Has abstractyes

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