Bibliographic record
Abstract
In this thesis, the framework within which long-term goals are set and subsequently achieved or approached is analyzed. Sustainable development and climate change are areas in which goals have tobe set despite uncertainties. The analysis is divided into the normative motivations for setting such goals, what forms of goals could be set given the empirical and normative uncertainties, and how tomanage doubts regarding achievability or values after a goal has been set.Paper I discusses a set of questions that moral theories intended to guide goal-setting should respond to. It is often claimed that existent normative theories provide only modest guidance regarding climate change, and consequently have to be revised or supplemented. Two such suggested revisions or supplements are analyzed in order to determine whether they provide such guidance.Paper II applies the deep ecological framework to survey the extent to which it can be utilized to discuss issues concerning the management of climate change. It is suggested that the deep ecological framework can provide guidance by establishing a normative framework and an analysis of how the overarching values and principles can be specified to be relevant for actions.Paper III is focused on normative political theory, and explicates the two dimensions of empirical and normative uncertainty. By applying recent discussions in normative political theory on ideal/non-ideal theory, political realism, and the relation between normative demands and empirical constraints,strategies for managing the proposed goals are suggested.Paper IV suggests a form of goal that incorporates uncertainties. Cautious utopias allow greater uncertainty than realistic goals (goals that are known to be achievable or approachable, and desirable),but not to the same extent as utopian goals (goals wherein it is highly uncertain whether the goal can actually be achieved). Such goals have a performance-enhancing function. A definition and quality criteria for such goals are proposed.Paper V considers whether a goal that is becoming all the more unlikely to be achievable should be reconsidered. The paper focuses on the two degrees Celsius target, and asks whether it could still be a sensible goal to aspire to. By applying the principle that ‘ought’ implies ‘can’, the role of such obligations is investigated.Paper VI surveys how to treat circumstances in which an already set goal should be reconsidered and possibly revised, and what would evoke doubt in the belief upon which those goals have been set.Two situations are analyzed: (i) a problematic or surprising event occurs, upsetting confidence in one’s relevant beliefs, or (ii) respectable but dissenting views are voiced concerning one’s means and/or values. It is suggested that the validity of doubt has to be considered, in addition to the level in a goal-means hierarchy towards which doubt is raised.
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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 | 0.000 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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; both teacher heads agree on what is shown here.
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".