Leading International Baccalaureate Diploma Program Schools in Developing Countries
Bibliographic record
Abstract
Over the preceding decade, the number of schools offering the International Baccalaureate Diploma Programme (IBDP) has grown at a rapid rate. As of 2014, there were almost 2,500 schools across 137 countries offering the IBDP to students around the world (IBO, 2014a). The majority of these schools remain in developed countries, especially in the Asia Pacific region, North America, and Western Europe. In recent years, however, there has been strong growth of IBDP schools in developing countries. Consequently, based on World Bank (2014) classifications, over one quarter (28%) of IBDP schools worldwide are now located in developing countries (IBO, 2014a). Despite this, there remains a dearth of studies on the IBDP in developing countries, and a particular scarcity of research in the field of school leadership. This is a concern, given first, the growing academic literature identifying the importance of leadership for successful implementation of the IBDP (e.g., Hall, Elder, Thompson, Pollack, 2009; Hallinger, Walker, Lee, 2010; Lee, Hallinger, Walker, 2012a) and second, recognition of the significance of context for understanding specific challenges faced by school leaders (e.g., Bossert, Dwyer, Rowan, Lee, 1982; Goldring, Huff, May, Camburn, 2008; Hallinger, Walker, Bajunid, 2005). In response, this chapter provides a review of the literature regarding IBDP school leadership in developing countries. More specifically, the chapter will outline the key findings of 11 studies (8 empirical and 3 discussion based) related to the issues and challenges to IBDP school leadership in Argentina and Chile (Resnik, 2012), China (Bunnell, 2008; Lee et al., 2014; Walker et al., 2014), China, Hong Kong (not a developing country), Thailand, and Vietnam (Hallinger et al., 2010), Ecuador (Barnett, 2013), India (Mirza, 2011; Ranger, 2012; Sen, 2001), Mauritius (Poonoosamy, 2010), Mexico (Saavedra, Lavore, Flores, 2014), and Turkey (Halicioğlu, 2008). In this process, the chapter will examine the similarities and differences in contextual factors affecting school leadership among these 11 studies. Particularly, the chapter will pay close attention to the findings of a recent multisite case study of IBDP schools in China (Lee et al., 2013). We also take heed of the findings of two follow up studies to Lee et al.’s research (see Wright Lee, 2014a; Wright Lee, 2014b). This is in response to the growing importance of China as a market for the IBDP, alongside a lack of previous research in the Chinese context. To achieve this, a conceptual framework, adapted from Lee, Hallinger, and Walker (2012b), will be utilized, which distinguishes between “environmental” (external) and “organizational” (internal) contextual school leadership challenges.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".