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Record W7124959137 · doi:10.5281/zenodo.18318006

Mediji in predšolski otroci v Sloveniji 2022

2022· dataset· sl· W7124959137 on OpenAlexaboutno aff
Mateja Rek, Predrag Ljubotina, Anja Bratoš, Sabina Mešić

Bibliographic record

VenueZenodo (CERN European Organization for Nuclear Research) · 2022
Typedataset
Languagesl
FieldSocial Sciences
TopicChild Development and Digital Technology
Canadian institutionsnot available
Fundersnot available
KeywordsData collectionPresentation (obstetrics)Quarter (Canadian coin)Descriptive statisticsScale (ratio)LiteracyStatistical analysis

Abstract

fetched live from OpenAlex

The collected data, presented in the report MEDIA AND PRESCHOOL CHILDREN IN SLOVENIA, includes the views, opinions, assessments of parents and educators of children aged from 11 months to inclusively 6 years old, who are enrolled in the preschool education and care system in Slovenia in 2022. The accumulation of statistical base focused on the aforementioned vulnerable group has taken place on such a scale within the Infrastructure Program of the Faculty of Media once before. Therefore, the already existing findings highlighted in Media and preschool children by Rek, Milanovski Brumat from 2016, joined with the results of current analyzes form a significant contribution to society, since the expansion of the mentioned database, whose qualities are among other its quantity and period of the data assembly, assure and support many possibilities of further research work. At the same time, the inventory of the state and trends in the current time and on the national level increases the possibility of adequate planning of media literacy guidelines for all segments of society in various disciplines and sciences. The collection of data on the media literacy of preschool children in Slovenia takes place in the second quarter of 2022 and included a review of relevant literature and existing databases, preparation and testing of a survey questionnaire, acquisition, editing, archiving and analysis of data using descriptive statistics methods and conclusively a presentation of the results in the form of a final report. We focused on the media habits that preschool children develop and the upbringing they receive. We have identified two key environments of influence; home and kindergarten, and consequently accumulated answers from the target groups of their parents and educators. Using a measuring instrument, an online questionnaire, we obtained estimates of the timeframes of media and screen exposure of the respondents' preschool children and the respondents themselves. We checked the attitudes of parents and educators regarding the setting of limits, their observations in the context of health, mood, body weight, aggressiveness, and preschool children’s imitation of heroes. Respondents told us whether they were familiar with the recommendations regarding media use by preschool children. We asked whether the educators of the sample have clear guidelines regarding the use of media from the management, whether they have attended at least a two-hour education about media in the last two years, and whether the pediatrician talked to the parents of the sample about the impact of the media on the child before the child turned three years old. Furthermore, the set of questions referred to the area of respondents' reflexivity, their own verification of information obtained from various media and their personal trust in the media. Demographic characteristics of the sample and paradata are also part of the report. Key words: media, preschool children, childcare workers, parents, screens, limits, media education, media literacy

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)0.001
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: none
GenreCandidate signal: Dataset · Consensus signal: none
Teacher disagreement score0.019
Threshold uncertainty score0.065

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
Science and technology studies0.0020.001
Scholarly communication0.0050.001
Open science0.0000.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0190.003

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 head0.032
GPT teacher head0.270
Teacher spread0.238 · 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
GenreDataset

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
Published2022
Admission routes1
Has abstractyes

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