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Record W4205597132 · doi:10.4212/cjhp.v75i1.3256

To Go Far, Go Together

2022· article· en· W4205597132 on OpenAlexvenueno aff
Zack Dumont

Bibliographic record

VenueThe Canadian Journal of Hospital Pharmacy · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicDiaspora, migration, transnational identity
Canadian institutionsnot available
Fundersnot available
KeywordsGo/no goComputer science

Abstract

fetched live from OpenAlex

The proverb itself has stood the test of time: If you want to go fast, go alone; if you want to go far, go together.The adage is not clearly attributed to any one person, and perhaps this is intentional.It speaks to what leadership isn't, and in place beckons collaboration, cooperation, coordination, and engagement, tools leaders strive to use.Leadership is about others.An individual's specific credentials or qualifications do not guarantee success.A leader must find ways to create a particular environment; one where the collective competency of team members can optimally radiate.Further, when presented with an opportunity to lead, we learn that the spotlight will come-there are times when this will be welcome and others when it won't-thus, there's no need to force it.Instead, the opportunity can be used to shine the spotlight on others.Leadership is about listening to others.It's not about the leader getting first crack.To lean on another important saying: leaders eat last.It's still great for a leader to bring forth ideas, especially if they are innovative.However, an idea is not much good if the visionary can't help others see how the benefits may outweigh the risks.While we must work together to ensure we're attentive to every voice, it's up to those in leadership roles to communicate their vision in compelling and inspirational ways.This helps reach the state whereby a team can make informed decisions.On that note, it's not all about 'majority rule'.It's about first seeking consensus.Leadership is about all time.For example, we mustn't simply aim to balance this year's budget, we must also aim to balance next year's, and the year after that, and so on.It's about setting-up to have infinite budget cycles.It's critical not to throw away everything for today, as tomorrow is

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)0.003
metaresearch head score (Gemma)0.013
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 score0.111
Threshold uncertainty score0.242

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.013
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0380.015
Scholarly communication0.0170.016
Open science0.0020.016
Research integrity0.0070.018
Insufficient payload (model declined to judge)0.0720.028

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 head0.029
GPT teacher head0.319
Teacher spread0.289 · 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".

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Citations0
Published2022
Admission routes1
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