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Record W2529427039 · doi:10.1017/cbo9781139583800.006

Intensifiers: upping the ante – <i>super</i> cool!

2016· book-chapter· en· W2529427039 on OpenAlexaff
Sali A. Tagliamonte

Bibliographic record

VenueCambridge University Press eBooks · 2016
Typebook-chapter
Languageen
FieldArts and Humanities
TopicMedia Influence and Health
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsFront (military)Political scienceEngineeringMechanical engineering

Abstract

fetched live from OpenAlex

If I was talking to you on an ongoing basis I wouldn't use a swear word necessarily, but it seems to me the young people do that to a certain extent. Now I don't know whether that's more anecdotal. As a teacher you don't really hear that 'cause they don't swear in front of you. (Dirk Brooks, 73) Even though language is always changing, it is actually difficult to catch language change in action. This is because many of the changes language undergoes take place slowly over a very long period of time, sometimes thousands of years. However, some parts of a language change constantly – so continuously in fact, that sometimes you can stand a good chance of catching them while they are happening. This is a story about how I caught another language change in progress. Some of the best places to look for language change are those where people use words imaginatively. Slang terms are a good example. Most changes in the selection of words are lexical changes. If you want to tap into more systemic changes in progress a better place to look is within the grammatical devices of the language. In this study, I examined adverbs. Adverbs are words that add further meaning to other words. They can act to tone words down or boost them up. In the latter case they are called “intensifying” adverbs. Take for example a day that is colder than usual. How would you describe such a day? You can “up the ante,” so to speak, and say that it is very cold . You can also diminish the quality of an adjective as well. You could say, for example, it is kind of cold … sort of cold … or moderately cold. The words that tone down the nature of the cold are also adverbs, but in this case they are called down-toners, as in (66). a. That's kind of funny but, ah, it was good fun. (Craig Thompson, 18) b. So the neighborhood's changing a lot but it's still fairly well mixed. (Shannon Ermak, 19) c. It was kind of confusing but a lot of fun. (Cheryl Choi, 12) Listen out on days that are either bitterly cold or burning hot. You may hear all kinds of adverbs.

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)0.001
metaresearch head score (Gemma)0.002
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: Other
Teacher disagreement score0.043
Threshold uncertainty score0.143

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0040.004
Scholarly communication0.0050.008
Open science0.0010.003
Research integrity0.0010.006
Insufficient payload (model declined to judge)0.0430.019

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 head0.055
GPT teacher head0.207
Teacher spread0.151 · 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

Citations0
Published2016
Admission routes1
Has abstractyes

Explore more

Same venueCambridge University Press eBooksSame topicMedia Influence and HealthFrench-language works237,207