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Record W4255369621 · doi:10.1002/9781119176602.oth1

Conclusion

2015· other· en· W4255369621 on OpenAlexaboutno aff
Bart Egnal

Bibliographic record

Venuenot available
Typeother
Languageen
FieldSocial Sciences
TopicMultilingual Education and Policy
Canadian institutionsnot available
Fundersnot available
KeywordsCitationComputer scienceLibrary scienceLinguisticsPhilosophy

Abstract

fetched live from OpenAlex

The Future of a Radical Price, Wired magazine editor Chris Anderson explains how the rise of abundance in the world changes what we value -and what we don't.He notes that, "as commodities become cheaper, value moves elsewhere.There's still a lot of money in commodities . . .but the highest profit margins are usually found where gray matter has been added to things." 1 Anderson cites the music industry as a prime example of this shift.Never before has recorded music been so abundant and so easy to consume.It is easier to acquire it through downloading, easier for musicians to provide it to us, and in all respects it is cheaper than ever before.With this rise of abundance, value has moved to the thriving concert business (I blanch at what I paid recently for tickets to hear U2 play in Toronto).Anderson points out that, "Some bands, such as the Rolling Stones, make more than 90 percent of their money from touring . . . .And why not?Memorable experiences are the ultimate scarcity." 2 What do the changing economics of the music industry have to do with becoming an inspiring leader?Simple: the same abundance/ scarcity principle applies to both worlds.Today's business world is awash in communication.Our email inboxes are clogged, our Twitter feeds are ever-refreshing, and our LinkedIn contacts disgorge a daily dose of content.The intensity of most workplaces means more meetings, more conversations, and more interactions than ever before, and more superficial, information-based, jargon-ridden communication.Yet, in today's business world there is also an increasingly scarce commodity: clear, powerful, inspiring communication.As this information overload increases, so too does our need for inspiration.

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)0.004
metaresearch head score (Gemma)0.014
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
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 score0.820
Threshold uncertainty score0.000

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.014
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0040.002
Scholarly communication0.0100.006
Open science0.0020.004
Research integrity0.0050.005
Insufficient payload (model declined to judge)0.1800.040

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 head0.125
GPT teacher head0.548
Teacher spread0.424 · 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.

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
Published2015
Admission routes1
Has abstractyes

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