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Record W2130575998 · doi:10.1108/10878570210697513

Ten strategies for survival in the attention economy

2002· article· en· W2130575998 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueStrategy and Leadership · 2002
Typearticle
Languageen
FieldSocial Sciences
TopicDigital Marketing and Social Media
Canadian institutionsPricewaterhouseCoopers (Canada)
Fundersnot available
KeywordsAllianceBusinessCompetition (biology)MarketingDigital economyMarket powerPower (physics)Industrial organizationEconomicsMarket economyComputer scienceMonopolyPolitical science

Abstract

fetched live from OpenAlex

Vying for the attention of the time‐challenged, choice‐saturated consumer characterizes the thrust of competition for the coming five years. There will be a continuing explosion of new technologies and new ways to connect electronically. For the foreseeable future, demands for the consumer’s attention will continue to escalate. Consumers will gravitate to trusted brands to manage and filter the bombardment of choices. In the attention economy, therefore, consumers exert greater power to shape content and experiences. In response, companies will need to deploy a much more sophisticated electronic infrastructure to respond and, will also market to ever‐smaller, more finely targeted segments. Successful brand‐owning business models will form collaborative alliance networks. Development will center on strategies that capture the attention of selected targets through deep responsiveness to the consumer’s experience of the brand, and on new business models that respond proactively to the rise of digital media.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.851
Threshold uncertainty score0.278

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.215
GPT teacher head0.317
Teacher spread0.101 · 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