Differences in Motivation between Male and Female in Slovakia in 2015
Why this work is in the frame
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Bibliographic record
Abstract
<span style="font-family: Times New Roman; font-size: small;"> </span><p style="margin: 0cm 0cm 4pt; line-height: normal;"><span style="font-family: Times New Roman;"><span lang="EN-GB" style="font-size: 10pt; mso-ansi-language: EN-GB; mso-bidi-font-weight: bold;">Meeting human needs or life</span><span lang="EN-US" style="font-size: 10pt; mso-ansi-language: EN-US; mso-bidi-font-weight: bold;">’s challenges, internal and external environments as well as some further factors affect </span><span lang="EN-GB" style="font-size: 10pt; mso-ansi-language: EN-GB; mso-bidi-font-weight: bold;">motivation significantly.<span style="mso-spacerun: yes;"> </span>All factors are interconnected to each other and they create mutually connected parts of network. <span style="mso-spacerun: yes;"> </span></span><span lang="EN-GB" style="font-size: 10pt; mso-ansi-language: EN-GB;">In the paper we mention the issue of motivational differences between male and female in Slovakia in the year 2015. Sampling unit contains 4,099 respondents. <span style="mso-bidi-font-weight: bold;">Deep knowledge of the differences plays a key role in employee job performance and affects the employees’ motivation effectively. <span style="mso-spacerun: yes;"> </span>Results of the social inquiry confirm great similarity between motivation factors of male and female in Slovakia in 2015. </span>Despite small significant differences we can state that there is a possibility of creating unified motivation programme for employees regardless of gender. <span style="mso-spacerun: yes;"> </span>Specific gender differences in the level of motivation have to be taken into account in order to increase motivation. In the future meeting the needs of employees can cause the changes in their motivation requirements. Therefore we suggest the organisation to update motivation programme from time to time.</span><span lang="EN-GB" style="mso-ansi-language: EN-GB;"><span style="font-size: small;"> </span></span></span></p><span style="font-family: Times New Roman; font-size: small;"> </span>
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Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it