MétaCan
Menu
← Back to cohort
Record W4390906083 · doi:10.22495/cgpmpp3

Nomination committees in Iceland and Nordic comparison: An overview

2024· article· en· W4390906083 on OpenAlexaff
Þröstur Olaf Sigurjónsson, Murray Bryant, Hildur Magnúsdóttir

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCorporate Finance and Governance
Canadian institutionsWestern University
Fundersnot available
KeywordsNominationCorporate governancePolitical sciencePublic relationsBusinessAccountingPublic administrationLawFinance

Abstract

fetched live from OpenAlex

Nomination committees are becoming increasingly popular. A nomination committee, or a nominating board, is a group or committee responsible for selecting and nominating candidates for a company’s board of directors. The primary purpose of a nomination committee is to identify and recommend qualified individuals who can effectively fulfill the responsibilities of the positions in question. Still, nomination committees’ roles and work processes have not been much researched. Among those issues yet not solved is whether selection practices will be more professional and transparent by the existence of nomination committees. Nonetheless, according to guidelines on good corporate governance, there are existing arguments for how beneficial nomination committees can be for good governance practices. This research compares and presents similarities and differences regarding nomination committees in the Nordic countries. The Nordic countries, being similar in many ways, have not all taken the same path regarding nomination committees. Hence, it makes an interesting comparison study. Guidelines for governance are similar and, in all essentials, comparable to what is happening in the Nordic countries. Therefore, it must not be forgotten that companies can deviate from the guidelines’ recommendations as their circumstances require. It can be assumed that good governance, including nomination committees, is one of the things that companies should adopt more and more if considering the development in other countries, e.g., the Nordic countries. The activity of foreign investors has also led to jumps in the development of governance practices.

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)0.008
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.070
Threshold uncertainty score0.139

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0080.012
Science and technology studies0.0020.002
Scholarly communication0.0060.002
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.001

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 head0.053
GPT teacher head0.285
Teacher spread0.232 · 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 designObservational
Domainnot available
GenreEmpirical

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

Citations1
Published2024
Admission routes1
Has abstractyes

Explore more

Same topicCorporate Finance and Governance→French-language works237,207→