Bibliographic record
Abstract
This thesis includes three essays on news shocks and fiscal multipliers.In the second chapter, I demonstrate that a fiscal news shock originated from an increased defence spending in the U.S. can directly transmit to Canada in the form of an induced defence spending.As a long-term ally of the U.S. in geo-political events, Canada had intervened in several wars and global conflicts along side with the U.S. in the last several decades.News about a large defence spending change in the U.S. can affect Canadian defence policy.Consequently, this change in defence spending may have a significant economic implication for Canada.This paper proposes a new channel of fiscal news shock transmission from the U.S. to Canada labelled as induced spending channel.My transmission model shows that a U.S. defence spending news shock has a positive impact on Canadian GDP.I coin a novel multiplier labelled as international defence multiplier which can estimate the magnitude of the induced defence spending change of a country in response to the defence spending change of another country.In the third chapter, I explore whether the transfer payments to households boost private consumption spending across provinces in Canada.To estimate a causal relationship between transfer payments and consumption, I propose Universal Child Care Benefit payments across provinces in Canada as an instrument for transfer payments.Universal Child Care Benefit is a formula-based transfer where the total i First and foremost, I would like to express my sincere gratitude to my supervisor, Professor Christopher M. Gunn, for his invaluable guidance, support, and encouragement throughout the dissertation process.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 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; 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".