Social Media Marketing Communication of Educational Institutions in the First Quarter of 2023: The Example of the University of Latvia
Bibliographic record
Abstract
Bakalaura darba tēma ir “Izglītības iestāžu mārketinga komunikācija sociālajos medijos 2023. gada pirmajā ceturksnī: Latvijas Universitātes piemērs”. Darba mērķis ir noteikt, kā tiek uzturēta LU sociālo mediju komunikācijā, kā tos pamato atbildīgie speciālisti un kā tos novērtē auditorija. Darbs sastāv no teorētiskās, metodoloģiskās un empīriskās daļas. Teorija balstās uz akadēmiskiem darbiem par korporatīvo komunikāciju, integrēto mārketinga komunikāciju un tās realizēšanu sociālajos medijos. Metodoloģiskajā daļā tiek aplūkotas tādas pētniecības metodes, kā kvalitatīvā kontentanalīze, tiešsaistes aptauja un daļēji strukturētā intervija. Darba empīriskā daļa sastāv no konteksta informācijas par Latvijas izglītības iestāžu ainavu un LU, kā arī iegūto datu analīzes. Pētījuma rezultātā ir gūts ieskats Latvijas Universitātes mārketinga komunikācijas praksē sociālajos medijos.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.010 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.020 | 0.003 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".