Bibliographic record
Abstract
Abstract In addition to the challenges faced when delivering a marketing course to international students in general, the challenges are compounded when the students have little interest in the subject and the students are located in a country in transition. This study examines the experiences of the author in teaching marketing theory to first-year students at the Stockholm School of Economics, Riga, in the former Soviet Republic of Latvia. Although the political and economic systems in which the students were raised may have changed, the pre-transition period continues to have an influence on how marketing should be taught. Recommendations for international instructors wishing to teach in a transition economy are also discussed. Keywords: Transition economiesInternational teachingMarketingLatvia Notes 1. The ‘transition’ was from a centrally planned economic, political and social system to one based on democratic free markets and private ownership. Transition economy countries include the former Soviet Union, Central and Eastern Europe and to a lesser extent China, Vietnam and Cambodia. 2. Universities cover one or several significant fields completely and are entitled to confer doctoral degrees. According to decision of Latvian Council of Higher Education, the following institutions of higher education have university status: Daugavpils University, Latvia University of Agriculture, University of Latvia, Riga Stradrai University and Riga Technical University. 3. There was a degree of serendipity as to how the author was asked to teach this course. In 2004 the author attended a conference held at SSER and corresponded with the Rector of SSER if there was interest in having a lecture presented at the school. There was a positive response, and the author was taken to lunch after the presentation and asked about any interest in teaching the marketing theory section of the course. 4. In addition to the text-/theory-related requirements of the course, the students also had to create a marketing report on a local business—that part of the course was administered directly by SSER.
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 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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".