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Many believe that despite its destructive character, war ultimately boosts long-term economic growth. For the United States this view is often supported by appeal to the experience of the Second World War, understood as a triumph of both production and productivity. Alexander Field shows that between 1941 and 1945 manufacturing productivity actually declined, depressed by changes in the output mix and resource shocks from enemy action, including curtailed access to natural rubber and, on the Eastern Seaboard, petroleum. The war forced a shift away from producing goods in which the country had a great deal of experience toward those in which it had little. Learning by doing was only a partial counterbalance to the intermittent idleness and input hoarding that characterized a shortage economy and dragged down productivity. The conflict distorted human and physical capital accumulation and once it ended, America stopped producing most of the new goods. The war temporarily shut down basic scientific research and the ongoing development of civilian goods. U.S. world economic dominance in 1948, Field shows, was due less to the experience of making war goods and more to the country's productive potential in 1941

Intro -- Preface -- Contents -- 1 Introduction -- 1.1 The Turnpike Phenomenon for Convex Discrete-TimeProblems -- 1.2 The Turnpike Phenomenon -- 1.3 Turnpike Results for Problems in Metric Spaces -- 1.4 The Robinson-Solow-Srinivasan Model -- 2 The Description of the Robinson-Solow-Srinivasan Model and Its Basic Properties -- 2.1 The Robinson-Solow-Srinivasan Model -- 2.2 A Golden-Rule Stock -- 2.3 Good Programs -- 2.4 The von Neumann Facet -- 3 Infinite Horizon Optimization -- 3.1 Overtaking Optimal Programs -- 3.2 Auxiliary Results for Theorems 3.1-3.3 -- 3.3 Proofs of Theorems 3.1 and 3.2 -- 3.4 Proof of Theorem 3.3 -- 3.5 Examples -- 3.6 Convergence Results -- 3.7 Proof of Theorem 3.9 -- 3.8 Auxiliary Results for Theorems 3.10 and 3.11 -- 3.9 Proofs of Theorems 3.10 and 3.11 -- 3.10 The Structure of Good Programs in the RSS Model -- 3.11 Proofs of Theorems 3.16-3.18 -- 4 Turnpike Results for the Robinson-Solow-Srinivasan Model -- 4.1 The Main Results -- 4.2 Auxiliary Results for Theorems 4.2 and 4.3 -- 4.3 Four Lemmas -- 4.4 Proof of Theorem 4.2 -- 4.5 Proof of Theorem 4.3 -- 4.6 Extensions of Theorem 4.3 -- 4.7 Proof of Theorem 4.16 -- 4.8 Stability Results -- 4.9 Proof of Theorem 4.27 -- 5 The Robinson-Solow-Srinivasan Model with a Nonconcave Utility Function -- 5.1 Good Programs -- 5.2 Auxiliary Results -- 5.3 Properties of the Function U -- 5.4 Proofs of Theorems 5.4, 5.5 and 5.8 -- 5.5 Proof of Proposition 5.7 -- 5.6 Proof of Theorem 5.9 -- 5.7 The RSS Model with Discounting -- 5.8 An Auxiliary Result for Theorem 5.18 -- 5.9 Proof of Theorem 5.18 -- 5.10 Weakly Agreeable Programs -- 5.11 Proof of Theorem 5.22 -- 5.12 Auxiliary Results -- 5.13 Proof of Theorem 5.23 -- 5.14 Proof of Theorem 5.24 -- 5.15 Weakly Maximal Programs -- 5.16 Proof of Theorem 5.27 -- 5.17 Proof of Theorem 5.28 -- 5.18 Proof of Theorem 5.29.

Intro -- Preface -- Contents -- 1 Introduction -- 1.1 The Turnpike Phenomenon -- 1.2 Nonconcave (Nonconvex) Problems -- 1.3 Examples -- 1.4 Stability of the Turnpike Phenomenon -- 1.5 The RSS Model -- 1.6 Overtaking Optimal Programs for the RSS Model -- 1.7 Turnpike Properties of the RSS Model -- 1.8 Concluding Remarks -- 2 Infinite Horizon Nonautonomous Optimization Problems -- 2.1 The Model Description and Main Results -- 2.2 Upper Semicontinuity of Cost Functions -- 2.3 The Nonstationary RSS Model -- 2.4 Auxiliary Results for Theorems 2.4, 2.5 and 2.7 -- 2.5 Properties of the Function U -- 2.6 Proof of Theorem 2.4 -- 2.7 Proof of Theorem 2.5 -- 2.8 Proof of Theorem 2.7 -- 2.9 Overtaking Optimal Programs -- 2.10 Applications to the Nonstationary RSS Model -- 2.11 Auxiliary Results for Theorem 2.23 -- 2.12 Proof of Theorem 2.23 -- 3 One-Dimensional Concave RSS Model -- 3.1 Preliminaries and Main Results -- 3.2 Auxiliary Results -- 3.3 Proof of Theorem 3.14 -- 3.4 Stability Results -- 3.5 Proof of Theorem 3.26 -- 4 Turnpike Properties for Autonomous Problems -- 4.1 The Model Description and Main Results -- 4.2 A Controllability Lemma -- 4.3 TP Implies ATP -- 4.4 Two Auxiliary Results -- 4.5 ATP Implies TP -- 4.6 A Weak Turnpike Result -- 4.7 A Turnpike Result for Approximate Solutions -- 4.8 Auxiliary Results for Theorem 4.11 -- 4.9 Proof of Theorem 4.11 -- 4.10 Stability of the Turnpike Phenomenon -- 4.11 A Subclass of Models -- 4.12 Auxiliary Results -- 4.13 Proof of Theorem 4.20 -- 5 The Turnpike Phenomenon for Nonautonomous Problems -- 5.1 Preliminaries -- 5.2 A Turnpike Property -- 5.3 Examples -- 5.4 TP Implies (P1) and (P2) -- 5.5 Auxiliary Results -- 5.6 Completion of the Proof of Theorem 5.2 -- 5.7 A Turnpike Result for Approximate Solutions -- 5.8 An Auxiliary Result for Theorem 5.8 -- 5.9 Proof of Theorem 5.8.

Machine generated contents note:1.Party Discipline in Canada --2.Representation --3.Partisan Teams --4.The Communications Arena --5.Message Discipline --6.Government Centralization --7.Parliamentary Caucuses --8.Caucus Research Bureaus --9.Legislative Assemblies --10.Managing Trouble --11.The SNC-Lavalin Affair --12.Advice for a New Parliamentarian.

Intro -- Preface -- Contents -- 1 Introduction -- 1.1 Convex Discrete-Time Problems -- 1.2 The Turnpike Phenomenon -- 2 Infinite Horizon Optimal Control Problems -- 2.1 The Forest Management Problem -- 2.2 Infinite Horizon Problems Without Discounting -- 2.3 Auxiliary Results -- 2.4 Proofs of Theorems 2.6 and 2.7 -- 2.5 Proof of Theorem 2.9 -- 2.6 Proof of Theorem 2.10 -- 2.7 Infinite Horizon Problems with Discounting -- 2.8 An Auxiliary Result for Theorem 2.13 -- 2.9 Proof of Theorem 2.13 -- 2.10 An Application to the Forest Management Problem -- 3 Turnpike Properties -- 3.1 Preliminaries and Main Results -- 3.2 Auxiliary Results -- 3.3 Proof of Theorem 3.2 -- 3.4 Proof of Theorem 3.3 -- 3.5 Proof of Theorem 3.4 -- 3.6 Proof of Theorem 3.5 -- 3.7 Proof of Theorem 3.6 -- 3.8 Stability Results -- 3.9 Agreeable Programs -- 4 Generic Turnpike Properties -- 4.1 Preliminaries -- 4.2 Equivalence of the Turnpike Properties -- 4.3 Generic Results -- 5 Structure of Solutions in the Regions Close to the Endpoints -- 5.1 Preliminaries -- 5.2 Lagrange Problems -- 5.3 An Auxiliary Result for Theorem 5.13 -- 5.4 Proof of Theorem 5.13 -- 5.5 The First Class of Bolza Problems -- 5.6 An Auxiliary Result for Theorem 5.15 -- 5.7 Proof of Theorem 5.15 -- 5.8 The Second Class of Bolza Problems -- 5.9 Auxiliary Results for Theorem 5.17 -- 5.10 Proof of Theorem 5.17 -- 6 Applications to the Forest Management Problem -- 6.1 Preliminaries -- 6.2 Auxiliary Results -- 6.3 Turnpike Results -- 6.4 Generic Results -- References -- Index.

Persuading political party elites such as legislators and political staff to agree to a research interview is difficult. One successful technique to get past gatekeepers and increase willingness to participate in an interview is to work with an intermediary. This case study demonstrates that a senior colleague of potential interview participants can play a formidable role in establishing trustworthy connections and vouching for the main researcher. The research method was successfully used to collect insider information about an agreement to merge two major Canadian political parties. The newly constituted Conservative Party went on to form government, led by Prime Minister Stephen Harper who had a controlling communications style. Years later, Harper's former campaign manager agreed to my request to help recruit interview participants for a research study of the party merger that they collectively negotiated. Consequently, most of the senior political personnel involved with the merger negotiations agreed to be interviewed. Moreover, they spoke freely about the experience. While vouching can be a successful technique, it must be supplemented with more random ways of securing interviews. The case study also features a concrete example of media manipulation and why caution is warranted when relying on news reports for political research.

In this volume, critical scholars and educational activists explore the intricate dynamics between the enclosure of global commons and radical visions of a common social future that breaks through the logics of privatization, ecological degradation, and dehumanizing social hierarchies in education. In its institutional and informal configurations alike, education has been identified as perhaps the key stake in this struggle. Insisting on the urgency of an education that breaks free of the bonds of enclosure, the essays included in this volume weave together bright threads of radical thought into a vivid tapestry illustrating a critical framework for enacting a global educational commons. Alexander J. Means is Assistant Professor of Social and Psychological Foundations of Education, State University of New York College at Buffalo, USA. Derek R. Ford is Assistant Professor of Education Studies, DePauw University, USA.Graham B. Slater is Marriner S. Eccles Fellow in Political Economy at the University of Utah, USA. His research has appeared in theJournal of Education Policy,Educational Studies, andThe Review of Education, Pedagogy, and Cultural Studies.

"Canada's style of politics has changed in the last two decades. More recently, ministers have been kept on a short leash. MPs parrot the same catchphrases across the country. Journalists are shunned. Government information is hidden. Why is this happening? To get to the bottom of this, Alex Marland conducted over seventy interviews with political insiders, reviewed internal political files, and submitted dozens of access to information requests. He discovered that in the face of rapid changes in communication technology, the PMO relies on corporate marketing strategies to centrally control government messages and influence voters. At the core of the strategy is brand control; at stake is democracy as we know it."--